Machine learning on big data: Opportunities and challenges
Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advent of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms to uncove...
Uložené v:
| Vydané v: | Neurocomputing (Amsterdam) Ročník 237; s. 350 - 361 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
10.05.2017
|
| Predmet: | |
| ISSN: | 0925-2312, 1872-8286, 1872-8286 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advent of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms to uncover more fine-grained patterns and make more timely and accurate predictions than ever before; on the other hand, it presents major challenges to ML such as model scalability and distributed computing. In this paper, we introduce a framework of ML on big data (MLBiD) to guide the discussion of its opportunities and challenges. The framework is centered on ML which follows the phases of preprocessing, learning, and evaluation. In addition, the framework is also comprised of four other components, namely big data, user, domain, and system. The phases of ML and the components of MLBiD provide directions for identification of associated opportunities and challenges and open up future work in many unexplored or under explored research areas. |
|---|---|
| AbstractList | Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advert of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms to uncover more fine-grained patterns and make more timely and accurate predictions than ever before; on the other hand, it presents major challenges to ML such as model scalability and distributed computing. In this paper, we introduce a framework of ML on big data (MLBiD) to guide the discussion of its opportunities and challenges. The framework is centered on ML which follows the phases of preprocessing, learning, and evaluation. In addition, the framework is also comprised of four other components, namely big data, user, domain, and system. The phases of ML and the components of MLBiD provide directions for the identification of associated opportunities and challenges and open up future work in many unexplored or under explored research areas. Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advent of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms to uncover more fine-grained patterns and make more timely and accurate predictions than ever before; on the other hand, it presents major challenges to ML such as model scalability and distributed computing. In this paper, we introduce a framework of ML on big data (MLBiD) to guide the discussion of its opportunities and challenges. The framework is centered on ML which follows the phases of preprocessing, learning, and evaluation. In addition, the framework is also comprised of four other components, namely big data, user, domain, and system. The phases of ML and the components of MLBiD provide directions for identification of associated opportunities and challenges and open up future work in many unexplored or under explored research areas. |
| Author | Vasilakos, Athanasios V. Wang, Jianwu Pan, Shimei Zhou, Lina |
| Author_xml | – sequence: 1 givenname: Lina surname: Zhou fullname: Zhou, Lina email: zhoul@umbc.edu organization: Information Systems Department, UMBC, Baltimore, MD 21250, United States – sequence: 2 givenname: Shimei surname: Pan fullname: Pan, Shimei email: shimei@umbc.edu organization: Information Systems Department, UMBC, Baltimore, MD 21250, United States – sequence: 3 givenname: Jianwu surname: Wang fullname: Wang, Jianwu email: jianwu@umbc.edu organization: Information Systems Department, UMBC, Baltimore, MD 21250, United States – sequence: 4 givenname: Athanasios V. surname: Vasilakos fullname: Vasilakos, Athanasios V. email: athanasios.vasilakos@ltu.se organization: Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE-931 87 Skellefteå, Sweden |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61412$$DView record from Swedish Publication Index |
| BookMark | eNqFkL1OwzAURi1UJNrCGzDkAUjwT-IkHZCq8isVdQFWy3FuUlepHdkuiLcnVRADA0xXujrnG84MTYw1gNAlwQnBhF_vEgMHZfcJxSRPMEkw5SdoSoqcxgUt-ARNcUmzmDJCz9DM-x0eQELLKVo8S7XVBqIOpDPatJE1UaXbqJZBLqJN31sXDkYHDT6Spo7UVnYdmBb8OTptZOfh4vvO0ev93cvqMV5vHp5Wy3WsUpaHOKsVa6SSBAOmhBYSQ57VBOfAMU2zlDEuCauLqqlYRcuSAucNlsMjK_Km4WyOrsZd_wH9oRK903vpPoWVWtzqt6WwrhVdOAhOUkIHPB1x5az3DpofgWBxzCV2YswljrkEJmLINWiLX5rSQQZtTXBSd__JN6MMQ4d3DU54pcEoqLUDFURt9d8DX0B4iu8 |
| CitedBy_id | crossref_primary_10_1088_1742_6596_2386_1_012031 crossref_primary_10_1016_j_future_2020_07_020 crossref_primary_10_1186_s13673_019_0190_9 crossref_primary_10_1109_ACCESS_2023_3316508 crossref_primary_10_1088_1742_6596_1648_4_042100 crossref_primary_10_1007_s13132_020_00703_8 crossref_primary_10_1016_j_bdr_2021_100252 crossref_primary_10_1007_s11356_023_28507_8 crossref_primary_10_2478_amns_2023_2_01402 crossref_primary_10_1007_s44210_025_00055_5 crossref_primary_10_1155_2022_8200907 crossref_primary_10_1016_j_jspr_2025_102588 crossref_primary_10_1007_s40747_021_00583_8 crossref_primary_10_1051_e3sconf_202560501007 crossref_primary_10_3390_biomedicines13051073 crossref_primary_10_3390_s23041935 crossref_primary_10_3390_su17051924 crossref_primary_10_1080_09512748_2025_2462239 crossref_primary_10_1016_j_atech_2025_100848 crossref_primary_10_1177_1460458219900452 crossref_primary_10_1007_s10462_024_10816_0 crossref_primary_10_1016_j_cosrev_2023_100535 crossref_primary_10_3390_brainsci13081148 crossref_primary_10_1039_D4MO00008K crossref_primary_10_3390_ijgi10080561 crossref_primary_10_4018_IJDSST_292449 crossref_primary_10_1109_TIM_2025_3552383 crossref_primary_10_1007_s00607_021_00999_7 crossref_primary_10_3390_math11081767 crossref_primary_10_1007_s11269_024_03940_7 crossref_primary_10_1007_s42979_023_01838_6 crossref_primary_10_1016_j_envsoft_2024_106051 crossref_primary_10_1038_s41598_024_80648_z crossref_primary_10_1155_2022_5708652 crossref_primary_10_1002_ett_3272 crossref_primary_10_1016_j_ocecoaman_2024_107190 crossref_primary_10_1155_2022_3878072 crossref_primary_10_1016_j_eti_2023_103314 crossref_primary_10_1109_ACCESS_2024_3370911 crossref_primary_10_1016_j_jocs_2024_102523 crossref_primary_10_3233_IDT_180347 crossref_primary_10_3389_fpsyg_2022_1066317 crossref_primary_10_1049_ntw2_12077 crossref_primary_10_1016_j_future_2022_12_035 crossref_primary_10_1007_s12152_018_9371_x crossref_primary_10_1002_anbr_202400052 crossref_primary_10_1016_j_enconman_2019_112265 crossref_primary_10_1007_s44195_022_00026_y crossref_primary_10_1016_j_chemgeo_2025_122746 crossref_primary_10_1016_j_future_2024_107499 crossref_primary_10_1145_3686990 crossref_primary_10_1109_ACCESS_2025_3558091 crossref_primary_10_1007_s00784_025_06216_5 crossref_primary_10_3389_fams_2020_00030 crossref_primary_10_1007_s10462_022_10143_2 crossref_primary_10_1016_j_envpol_2025_126361 crossref_primary_10_1109_TRPMS_2022_3231702 crossref_primary_10_1007_s11227_023_05711_4 crossref_primary_10_1016_j_jii_2025_100904 crossref_primary_10_1145_3676961 crossref_primary_10_1007_s11220_019_0249_8 crossref_primary_10_3390_f14020317 crossref_primary_10_1016_j_procs_2025_04_642 crossref_primary_10_1093_plphys_kiaf132 crossref_primary_10_1109_ACCESS_2020_2990765 crossref_primary_10_4014_jmb_2411_11010 crossref_primary_10_1080_01431161_2025_2465916 crossref_primary_10_1016_j_bdr_2017_10_002 crossref_primary_10_1155_2022_1645204 crossref_primary_10_3847_1538_4357_ad0e6c crossref_primary_10_1007_s11356_024_35662_z crossref_primary_10_1007_s10664_024_10467_3 crossref_primary_10_1016_j_jhazmat_2024_136241 crossref_primary_10_1186_s43094_025_00794_7 crossref_primary_10_1016_j_jbusres_2019_10_053 crossref_primary_10_1109_ACCESS_2020_3046078 crossref_primary_10_3390_electronics13224405 crossref_primary_10_3390_sym14112344 crossref_primary_10_3390_app112110442 crossref_primary_10_1002_adom_202402405 crossref_primary_10_1186_s40537_019_0278_0 crossref_primary_10_12688_f1000research_165342_1 crossref_primary_10_7769_gesec_v14i7_1670 crossref_primary_10_1109_TNSM_2020_2980289 crossref_primary_10_1007_s11227_021_03865_7 crossref_primary_10_1016_j_phycom_2022_101643 crossref_primary_10_1186_s40537_021_00485_z crossref_primary_10_2478_amns_2023_2_00588 crossref_primary_10_1016_j_adhoc_2019_101881 crossref_primary_10_1016_j_rineng_2025_106002 crossref_primary_10_1155_2022_8344791 crossref_primary_10_1007_s10639_022_11068_7 crossref_primary_10_1016_j_jenvman_2025_127210 crossref_primary_10_1016_j_neunet_2020_05_009 crossref_primary_10_1109_ACCESS_2020_3007727 crossref_primary_10_1016_j_asoc_2021_108295 crossref_primary_10_1016_j_engappai_2023_107670 crossref_primary_10_32604_cmc_2023_030818 crossref_primary_10_1155_2021_2955215 crossref_primary_10_1038_s41566_023_01313_x crossref_primary_10_3390_rs17132280 crossref_primary_10_3390_electronics12071558 crossref_primary_10_1109_TETC_2021_3054761 crossref_primary_10_3389_fpsyg_2022_943071 crossref_primary_10_3390_electronics10050552 crossref_primary_10_1080_01621459_2023_2282644 crossref_primary_10_1080_10618600_2022_2084404 crossref_primary_10_1002_npp2_16 crossref_primary_10_1016_j_tourman_2020_104129 crossref_primary_10_3390_rs16142561 crossref_primary_10_1155_2022_9680205 crossref_primary_10_1016_j_nicl_2021_102694 crossref_primary_10_32604_cmc_2024_048528 crossref_primary_10_3390_s22124409 crossref_primary_10_1007_s00530_020_00733_x crossref_primary_10_1088_1742_6596_1345_2_022049 crossref_primary_10_1093_bib_bbaa257 crossref_primary_10_1016_j_heliyon_2023_e13368 crossref_primary_10_1155_2022_8100495 crossref_primary_10_1016_j_ijbiomac_2025_146666 crossref_primary_10_3390_app15105266 crossref_primary_10_3390_rs11151741 crossref_primary_10_1016_j_egyr_2024_02_036 crossref_primary_10_1057_s41270_023_00256_0 crossref_primary_10_3390_bdcc2030023 crossref_primary_10_1007_s11277_021_09362_7 crossref_primary_10_1680_jenge_24_00002 crossref_primary_10_1186_s13690_024_01386_2 crossref_primary_10_1016_j_cities_2019_05_024 crossref_primary_10_1007_s10953_024_01417_0 crossref_primary_10_1155_2022_8439888 crossref_primary_10_1088_1402_4896_ac098b crossref_primary_10_1186_s40537_019_0254_8 crossref_primary_10_3390_w16131904 crossref_primary_10_1016_j_tourman_2018_12_007 crossref_primary_10_1088_1742_6596_1533_4_042015 crossref_primary_10_3390_coatings15010049 crossref_primary_10_1007_s11053_025_10491_0 crossref_primary_10_1007_s12393_024_09386_2 crossref_primary_10_1109_TCE_2023_3329545 crossref_primary_10_1016_j_procs_2023_10_114 crossref_primary_10_1016_j_jhydrol_2020_125324 crossref_primary_10_1109_TSC_2019_2916416 crossref_primary_10_1002_dac_3772 crossref_primary_10_1364_OPTCON_566725 crossref_primary_10_3390_en18195066 crossref_primary_10_1021_acs_jcim_5c00159 crossref_primary_10_1063_5_0226435 crossref_primary_10_1007_s00521_020_05178_x crossref_primary_10_1109_JIOT_2022_3157552 crossref_primary_10_1002_ldr_5635 crossref_primary_10_3390_informatics12030061 crossref_primary_10_1007_s40725_024_00231_7 crossref_primary_10_1016_j_knosys_2023_110420 crossref_primary_10_1016_j_future_2018_04_032 crossref_primary_10_1016_j_commatsci_2020_110244 crossref_primary_10_1016_j_compag_2023_107994 crossref_primary_10_1016_j_envc_2025_101123 crossref_primary_10_1155_2021_5598001 crossref_primary_10_3390_app112411910 crossref_primary_10_1016_j_rineng_2024_102215 crossref_primary_10_1007_s00521_021_06443_3 crossref_primary_10_1016_j_seta_2022_102674 crossref_primary_10_1016_j_neucom_2022_04_083 crossref_primary_10_1109_JIOT_2023_3238038 crossref_primary_10_1145_3708497 crossref_primary_10_1016_j_ijmedinf_2022_104758 crossref_primary_10_5498_wjp_v12_i2_204 crossref_primary_10_1186_s40537_024_00914_9 crossref_primary_10_1186_s42400_023_00172_x crossref_primary_10_37745_ejcsit_2013_vol13n478694 crossref_primary_10_57020_ject_1475566 crossref_primary_10_1088_1742_6596_1302_3_032054 crossref_primary_10_1016_j_bas_2024_102835 crossref_primary_10_1016_j_inffus_2025_103221 crossref_primary_10_3390_app13063997 crossref_primary_10_1088_1742_6596_1168_3_032132 crossref_primary_10_1088_1742_6596_1911_1_012026 crossref_primary_10_3390_electronics13183716 crossref_primary_10_1016_j_autcon_2025_106294 crossref_primary_10_1007_s43681_024_00601_8 crossref_primary_10_3390_soc15030054 crossref_primary_10_3390_jpm14090981 crossref_primary_10_3390_math12020182 crossref_primary_10_1007_s11269_023_03690_y crossref_primary_10_1016_j_physleta_2024_130065 crossref_primary_10_3390_ma15207187 crossref_primary_10_1007_s12065_024_00934_7 crossref_primary_10_2478_fiqf_2025_0004 crossref_primary_10_1007_s41870_022_01141_2 crossref_primary_10_1155_2021_5554444 crossref_primary_10_1097_CIN_0000000000000463 crossref_primary_10_3390_jpm12060908 crossref_primary_10_3390_rs17101679 crossref_primary_10_3390_buildings15060865 crossref_primary_10_1002_gch2_202100084 crossref_primary_10_1016_j_nucengdes_2024_113307 crossref_primary_10_1002_sys3_4 crossref_primary_10_1155_2022_6922554 crossref_primary_10_3390_math12152376 crossref_primary_10_1002_agj2_21358 crossref_primary_10_1016_j_procs_2023_01_381 crossref_primary_10_1016_j_ijinfomgt_2018_08_006 crossref_primary_10_1109_ACCESS_2020_2984778 crossref_primary_10_1108_MEDAR_04_2018_0325 crossref_primary_10_1007_s10845_024_02482_4 crossref_primary_10_1016_j_cageo_2022_105248 crossref_primary_10_1155_2022_8697421 crossref_primary_10_1109_ACCESS_2019_2962525 crossref_primary_10_1016_j_watres_2024_123041 crossref_primary_10_1007_s42484_024_00194_9 crossref_primary_10_1109_ACCESS_2020_2970143 crossref_primary_10_3390_agronomy15010200 crossref_primary_10_1016_j_chemosphere_2024_141462 crossref_primary_10_2478_amns_2024_2419 crossref_primary_10_1155_2021_3870147 crossref_primary_10_1080_15389588_2025_2527849 crossref_primary_10_1109_ACCESS_2022_3195338 crossref_primary_10_3390_app14135845 crossref_primary_10_1016_j_ecoser_2022_101478 crossref_primary_10_1109_TPS_2019_2918157 crossref_primary_10_1109_TG_2024_3411154 crossref_primary_10_1007_s41748_025_00582_6 crossref_primary_10_26599_BDMA_2023_9020028 crossref_primary_10_1016_j_engappai_2025_110664 crossref_primary_10_1007_s11071_021_06416_0 crossref_primary_10_1155_2021_7998417 crossref_primary_10_3390_a17070316 crossref_primary_10_1016_j_esi_2025_06_001 crossref_primary_10_1016_j_compbiomed_2024_109281 crossref_primary_10_3233_JIFS_220017 crossref_primary_10_3390_s25061642 crossref_primary_10_1016_j_measurement_2025_116857 crossref_primary_10_1016_j_seta_2024_104097 crossref_primary_10_1007_s10639_023_12267_6 crossref_primary_10_3390_rs16163095 crossref_primary_10_1155_2022_2907393 crossref_primary_10_3390_informatics4030016 crossref_primary_10_1016_j_ifacol_2024_07_288 crossref_primary_10_1016_j_jhydrol_2024_131220 crossref_primary_10_1016_j_enbuild_2024_115061 crossref_primary_10_1142_S0219649219500096 crossref_primary_10_1007_s00521_023_09194_5 crossref_primary_10_1093_biostatistics_kxad032 crossref_primary_10_5668_JEHS_2025_51_3_137 crossref_primary_10_1145_3718364 crossref_primary_10_3390_d14090706 crossref_primary_10_1016_j_apmate_2025_100333 crossref_primary_10_1016_j_patcog_2018_09_018 crossref_primary_10_3389_fmars_2024_1492572 crossref_primary_10_1145_3427476 crossref_primary_10_3390_ijerph19138111 crossref_primary_10_1016_j_compbiomed_2023_107825 crossref_primary_10_1109_ACCESS_2019_2930410 crossref_primary_10_1038_s41598_025_96112_5 crossref_primary_10_1186_s13673_020_00231_z crossref_primary_10_3390_en16207093 crossref_primary_10_3390_make6020059 crossref_primary_10_1007_s10681_025_03522_7 crossref_primary_10_26634_jfet_19_4_20913 crossref_primary_10_1080_15481603_2025_2514330 crossref_primary_10_1109_ACCESS_2020_3017135 crossref_primary_10_4018_IJFC_2018070102 crossref_primary_10_1016_j_watres_2022_118494 crossref_primary_10_1016_j_matt_2022_10_007 crossref_primary_10_1007_s11227_020_03533_2 crossref_primary_10_1049_ipr2_70107 crossref_primary_10_1186_s12913_024_11932_x crossref_primary_10_3847_1538_3881_ad4da5 crossref_primary_10_1177_1094342020957393 crossref_primary_10_1007_s10489_022_03826_4 crossref_primary_10_1007_s11831_025_10324_6 crossref_primary_10_1155_2022_5604032 crossref_primary_10_7717_peerj_cs_2418 crossref_primary_10_1016_j_buildenv_2024_111553 crossref_primary_10_1155_2022_3499799 crossref_primary_10_7717_peerj_cs_1567 crossref_primary_10_1109_ACCESS_2022_3207173 crossref_primary_10_1016_j_watres_2022_119349 crossref_primary_10_1016_j_enbuild_2024_114072 crossref_primary_10_3390_app14062556 crossref_primary_10_3390_socsci12030118 crossref_primary_10_1016_j_fuel_2025_136065 crossref_primary_10_1016_j_neucom_2019_01_087 crossref_primary_10_3390_make7010013 crossref_primary_10_1016_j_neunet_2024_107016 crossref_primary_10_1108_EMJB_11_2021_0168 crossref_primary_10_1016_j_hcc_2023_100124 crossref_primary_10_1097_QAD_0000000000004193 crossref_primary_10_1007_s11053_017_9345_4 crossref_primary_10_1016_j_knosys_2018_10_009 crossref_primary_10_1016_j_ipm_2018_01_010 crossref_primary_10_1038_s41598_024_84550_6 crossref_primary_10_1109_MNET_001_1800540 crossref_primary_10_1109_TSMC_2022_3198833 crossref_primary_10_1109_TQE_2022_3169987 crossref_primary_10_1007_s12559_018_9613_6 crossref_primary_10_1016_j_ijpvp_2021_104471 crossref_primary_10_1016_j_neucom_2022_01_015 crossref_primary_10_1007_s11831_023_09996_9 crossref_primary_10_1016_j_jhazmat_2024_134666 crossref_primary_10_1038_s41746_024_01383_3 crossref_primary_10_3390_pharmaceutics16121559 crossref_primary_10_1016_j_technovation_2023_102713 crossref_primary_10_1115_1_4067270 crossref_primary_10_2139_ssrn_5256050 crossref_primary_10_1109_ACCESS_2018_2805680 crossref_primary_10_1007_s10489_019_01450_3 crossref_primary_10_1111_ffe_13816 crossref_primary_10_2166_hydro_2025_258 crossref_primary_10_1016_j_ins_2018_02_011 crossref_primary_10_1093_pnasnexus_pgae186 crossref_primary_10_1007_s11831_025_10342_4 crossref_primary_10_1007_s10334_024_01157_8 crossref_primary_10_1007_s11276_022_03177_5 crossref_primary_10_1016_j_eswa_2024_124936 crossref_primary_10_1016_j_tourman_2022_104689 crossref_primary_10_3390_electronics11193177 crossref_primary_10_1007_s42979_024_03046_2 crossref_primary_10_3389_fnagi_2025_1542514 crossref_primary_10_1038_s44172_023_00079_y crossref_primary_10_1088_2632_2153_acefaa crossref_primary_10_1016_j_eti_2021_101390 crossref_primary_10_1016_j_cclet_2024_110722 crossref_primary_10_3390_electronics14071357 crossref_primary_10_1186_s40537_020_00298_6 crossref_primary_10_1016_j_est_2023_107232 crossref_primary_10_1155_2022_9152605 crossref_primary_10_1007_s13218_017_0517_5 crossref_primary_10_1080_17517575_2020_1790043 crossref_primary_10_1016_j_applthermaleng_2025_125694 crossref_primary_10_3390_infrastructures9050078 crossref_primary_10_1007_s10651_024_00642_6 crossref_primary_10_1007_s41870_024_01991_y crossref_primary_10_3233_IDT_230247 crossref_primary_10_1109_ACCESS_2025_3569559 crossref_primary_10_1016_j_cageo_2023_105434 crossref_primary_10_3390_data6070077 crossref_primary_10_1016_j_rser_2022_112128 crossref_primary_10_3390_app11062726 crossref_primary_10_3390_electronics13183675 crossref_primary_10_1587_transinf_2020BDP0002 crossref_primary_10_1016_j_wasman_2024_12_023 crossref_primary_10_1016_j_jhydrol_2025_132883 crossref_primary_10_3390_cancers13153662 crossref_primary_10_1007_s11042_017_5045_7 crossref_primary_10_1016_j_scitotenv_2025_179678 crossref_primary_10_1080_00207543_2025_2532147 crossref_primary_10_32604_jbd_2022_028363 crossref_primary_10_1016_j_cej_2025_161299 crossref_primary_10_1108_JIMA_07_2021_0234 crossref_primary_10_1016_j_scitotenv_2024_170209 crossref_primary_10_1016_j_psep_2022_12_054 crossref_primary_10_1016_j_isatra_2024_10_002 crossref_primary_10_1093_rpd_ncaf064 crossref_primary_10_1007_s11053_017_9357_0 crossref_primary_10_3390_healthcare12070706 crossref_primary_10_3390_biomedicines10102472 crossref_primary_10_1007_s10639_022_11280_5 crossref_primary_10_1016_j_lwt_2021_112701 crossref_primary_10_1016_j_enconman_2019_111975 crossref_primary_10_1016_j_knosys_2018_10_033 crossref_primary_10_1016_j_cofs_2022_100873 crossref_primary_10_1080_10447318_2023_2262270 crossref_primary_10_3390_agronomy12030748 crossref_primary_10_1186_s40537_023_00703_w crossref_primary_10_1016_j_rineng_2025_105412 crossref_primary_10_1002_widm_70043 crossref_primary_10_1016_j_compeleceng_2024_109565 crossref_primary_10_1016_j_ecoenv_2022_113874 crossref_primary_10_1155_2018_6326049 crossref_primary_10_3390_jmse12091656 crossref_primary_10_1108_QRAM_04_2021_0063 crossref_primary_10_3390_rs15174168 crossref_primary_10_3390_su151310543 crossref_primary_10_3390_electronics12040871 crossref_primary_10_1371_journal_pone_0324644 crossref_primary_10_1210_clinem_dgae628 crossref_primary_10_3390_su131910743 crossref_primary_10_1155_2020_1357853 crossref_primary_10_3390_aerospace12070602 crossref_primary_10_1038_s41597_022_01639_1 crossref_primary_10_2196_18585 crossref_primary_10_1007_s13347_024_00782_4 crossref_primary_10_1080_17445760_2023_2225854 crossref_primary_10_3233_JIFS_169596 crossref_primary_10_1007_s11227_020_03328_5 crossref_primary_10_1016_j_neunet_2024_106699 crossref_primary_10_1089_hs_2019_0020 crossref_primary_10_17816_DD634885 crossref_primary_10_1155_2022_4116659 crossref_primary_10_1007_s11270_025_07954_8 crossref_primary_10_1145_3469028 crossref_primary_10_3390_rs13193906 crossref_primary_10_1016_j_procs_2024_08_075 crossref_primary_10_1038_s41598_025_09856_5 crossref_primary_10_1007_s12152_022_09498_8 crossref_primary_10_3389_fsoc_2022_886498 crossref_primary_10_1007_s00521_020_04861_3 crossref_primary_10_1016_j_elerap_2017_12_003 crossref_primary_10_3390_math12101601 crossref_primary_10_3390_math11132804 crossref_primary_10_1016_j_agwat_2024_108710 crossref_primary_10_1007_s40996_025_01956_6 crossref_primary_10_1016_j_ins_2019_12_054 crossref_primary_10_1016_j_apenergy_2019_114367 crossref_primary_10_3390_electronics14010066 crossref_primary_10_3389_fpsyg_2024_1479269 crossref_primary_10_3390_met11050690 crossref_primary_10_1016_j_ecoinf_2024_102950 crossref_primary_10_1177_14780771231181237 crossref_primary_10_3390_electronics10192350 crossref_primary_10_1155_2022_4945918 crossref_primary_10_3390_en13164158 crossref_primary_10_1080_14765284_2025_2549237 crossref_primary_10_1155_2022_8213895 crossref_primary_10_3390_s21196688 crossref_primary_10_3389_fpsyt_2025_1548287 crossref_primary_10_1007_s11227_025_07563_6 crossref_primary_10_3390_cryst15060538 crossref_primary_10_1080_19349637_2025_2454427 crossref_primary_10_1016_j_eswa_2025_129014 crossref_primary_10_1109_ACCESS_2022_3161511 crossref_primary_10_1109_ACCESS_2023_3308698 crossref_primary_10_1155_2022_5191929 crossref_primary_10_1002_cmtd_202500069 crossref_primary_10_1155_2022_4879361 crossref_primary_10_3390_electronics11040610 crossref_primary_10_3390_sym12040495 crossref_primary_10_1016_j_imu_2023_101428 crossref_primary_10_1155_2022_9513357 crossref_primary_10_1007_s10844_022_00775_9 crossref_primary_10_1016_j_earscirev_2025_105209 crossref_primary_10_1186_s42162_025_00524_6 crossref_primary_10_3390_drones7030214 crossref_primary_10_3390_jmse13010032 crossref_primary_10_1007_s00500_023_08041_y crossref_primary_10_3390_electronics12102287 crossref_primary_10_1016_j_ecoinf_2025_103187 crossref_primary_10_3390_ijerph17207650 crossref_primary_10_3390_jrfm18020103 crossref_primary_10_1007_s12145_025_01794_0 crossref_primary_10_1109_ACCESS_2020_2988120 crossref_primary_10_1145_3652596 crossref_primary_10_1109_ACCESS_2024_3393856 crossref_primary_10_1016_j_rser_2025_115895 crossref_primary_10_1145_3701041 crossref_primary_10_1016_j_rser_2020_110114 crossref_primary_10_1016_j_trd_2024_104067 crossref_primary_10_1016_j_oceaneng_2022_110689 crossref_primary_10_3390_rs16060956 crossref_primary_10_1007_s10845_019_01531_7 crossref_primary_10_1111_gec3_12563 crossref_primary_10_3390_bdcc4040034 crossref_primary_10_1109_ACCESS_2020_3039508 crossref_primary_10_1093_nar_gkz1208 crossref_primary_10_1088_1742_6596_1477_2_022038 crossref_primary_10_1038_s41598_023_37129_6 crossref_primary_10_1007_s42979_023_01809_x crossref_primary_10_3390_app11031173 crossref_primary_10_1007_s00521_025_11385_1 crossref_primary_10_1016_j_compbiolchem_2025_108680 crossref_primary_10_1093_comjnl_bxab135 crossref_primary_10_1016_j_cherd_2021_08_013 crossref_primary_10_1080_08839514_2021_1935591 crossref_primary_10_3390_ani15050687 crossref_primary_10_1108_JEIM_08_2019_0222 crossref_primary_10_1016_j_engappai_2024_108783 crossref_primary_10_1016_j_ijepes_2025_111065 crossref_primary_10_1007_s11760_019_01527_z crossref_primary_10_1038_s44172_024_00201_8 crossref_primary_10_1007_s00521_023_08960_9 crossref_primary_10_1080_10589759_2024_2375565 crossref_primary_10_1038_s41467_020_15734_7 crossref_primary_10_1016_j_inffus_2023_102217 crossref_primary_10_1016_j_ijinfomgt_2020_102231 crossref_primary_10_1080_09537325_2020_1732912 crossref_primary_10_1109_JRFID_2025_3575098 crossref_primary_10_1007_s10489_018_1235_x crossref_primary_10_1016_j_compeleceng_2023_108601 crossref_primary_10_1007_s41870_024_02028_0 crossref_primary_10_1016_j_cmpb_2021_106590 crossref_primary_10_1016_j_optmat_2025_116783 crossref_primary_10_1109_OJCS_2025_3579522 crossref_primary_10_3390_infrastructures9120213 crossref_primary_10_1109_TAI_2021_3100456 crossref_primary_10_1155_2022_4017151 crossref_primary_10_1155_2022_1172405 crossref_primary_10_1109_ACCESS_2025_3553934 crossref_primary_10_1002_cpe_6661 crossref_primary_10_1016_j_jechem_2023_02_028 crossref_primary_10_1155_2022_5346995 crossref_primary_10_3390_rs13224712 crossref_primary_10_1007_s10489_020_01952_5 crossref_primary_10_1038_s41598_025_00167_3 crossref_primary_10_1155_2022_8178963 crossref_primary_10_1016_j_ijhydene_2024_02_309 crossref_primary_10_1140_epjs_s11734_021_00207_9 crossref_primary_10_1177_01655515221133528 crossref_primary_10_1016_j_procs_2020_04_061 crossref_primary_10_1186_s42400_024_00341_6 crossref_primary_10_3390_computation8030080 crossref_primary_10_1016_j_jdent_2024_105260 crossref_primary_10_1016_j_jbiotec_2023_10_005 crossref_primary_10_1155_2022_2389636 crossref_primary_10_1007_s13042_019_01055_9 crossref_primary_10_3390_rs16213989 crossref_primary_10_1007_s10894_025_00495_2 crossref_primary_10_1016_j_neucom_2018_10_088 crossref_primary_10_1016_j_engappai_2025_111082 crossref_primary_10_1016_j_humov_2025_103381 crossref_primary_10_1007_s10270_025_01306_0 crossref_primary_10_1016_j_jclepro_2024_144171 crossref_primary_10_3390_sym12091526 crossref_primary_10_1080_21693277_2022_2155263 crossref_primary_10_3390_a18050253 crossref_primary_10_1002_adma_202104113 crossref_primary_10_1109_ACCESS_2023_3287861 crossref_primary_10_1038_s41598_024_58241_1 crossref_primary_10_3390_esa2030011 crossref_primary_10_1016_j_cmpb_2024_108294 crossref_primary_10_1016_j_compbiomed_2021_104305 crossref_primary_10_1007_s11227_018_2618_9 crossref_primary_10_1016_j_jhazmat_2025_137102 crossref_primary_10_1016_j_rser_2025_115980 crossref_primary_10_1145_3603707 crossref_primary_10_1111_add_70132 crossref_primary_10_3390_computers14070294 crossref_primary_10_1016_j_atech_2025_101219 crossref_primary_10_1016_j_datak_2019_06_004 crossref_primary_10_1109_ACCESS_2019_2910224 crossref_primary_10_3390_s22218371 crossref_primary_10_3389_fpls_2025_1579355 crossref_primary_10_1007_s00253_022_11963_6 crossref_primary_10_1109_ACCESS_2020_3001275 crossref_primary_10_2196_73212 crossref_primary_10_1002_cpe_6968 crossref_primary_10_1007_s00521_020_05102_3 crossref_primary_10_1007_s10586_018_2385_7 crossref_primary_10_3390_app10144901 crossref_primary_10_1016_j_desal_2024_118322 crossref_primary_10_1016_j_bspc_2023_105263 crossref_primary_10_36660_abc_20200596 crossref_primary_10_1016_j_kscej_2025_100250 crossref_primary_10_1016_j_seta_2019_100582 crossref_primary_10_1016_j_heliyon_2024_e37571 crossref_primary_10_1016_j_fss_2019_05_009 crossref_primary_10_1016_j_radi_2024_08_005 crossref_primary_10_3390_md20010038 crossref_primary_10_1109_TCYB_2022_3208130 crossref_primary_10_1007_s10462_019_09738_z crossref_primary_10_1016_j_est_2020_101409 crossref_primary_10_1155_2022_4553446 crossref_primary_10_3390_bs15030345 crossref_primary_10_1007_s00500_019_03901_y crossref_primary_10_4103_1673_5374_382228 crossref_primary_10_1016_j_jksuci_2017_12_007 crossref_primary_10_1109_ACCESS_2022_3204114 crossref_primary_10_3390_app13031296 crossref_primary_10_1007_s42979_020_00356_z crossref_primary_10_1155_2022_6082280 crossref_primary_10_3390_coatings15060693 crossref_primary_10_3390_s23010426 crossref_primary_10_3390_s23042112 crossref_primary_10_1142_S1793984425300067 crossref_primary_10_1007_s13278_018_0500_7 crossref_primary_10_1155_2022_5658641 crossref_primary_10_3389_frai_2021_576892 crossref_primary_10_1002_asi_24642 crossref_primary_10_1155_2022_9687496 crossref_primary_10_1007_s12530_018_9223_2 crossref_primary_10_1080_15391523_2023_2266060 crossref_primary_10_1016_j_est_2020_101410 crossref_primary_10_3390_app15010346 crossref_primary_10_3390_buildings14113515 crossref_primary_10_1155_2022_1761154 crossref_primary_10_1016_j_ecolmodel_2023_110476 crossref_primary_10_1016_j_watres_2023_120075 |
| Cites_doi | 10.1145/2647868.2654926 10.1186/s40537-014-0007-7 10.1007/s12559-016-9404-x 10.1109/ISCAS.2010.5537907 10.1109/ICDM.2012.155 10.1007/s10115-007-0073-7 10.1145/2783258.2783387 10.1145/2287076.2287111 10.1023/A:1007563306331 10.1007/s13748-012-0035-5 10.1109/BDC.2014.10 10.1016/j.neucom.2016.09.042 10.1145/2487788.2488042 10.14778/2556549.2556553 10.1145/2509352.2509396 10.1016/j.asoc.2015.01.035 10.1109/ICDCSW.2014.14 10.1109/BigData.Congress.2014.14 10.1145/1273496.1273641 10.1109/BigData.2016.7841037 10.1109/TPAMI.2013.50 10.1145/2647868.2654889 10.1109/ICDCS.2015.40 10.1016/j.neucom.2013.04.017 10.1109/ACCESS.2014.2325029 10.1126/science.aaa8415 10.1007/s10586-014-0360-5 10.1016/j.jpdc.2014.09.005 10.1007/11925231_54 10.14778/2735471.2735474 10.1109/TPAMI.2005.77 10.1504/IJCIH.2015.069788 10.14778/2733004.2733075 10.18653/v1/D13-1170 10.1016/j.neucom.2015.09.116 10.1109/TBDATA.2015.2472014 10.1145/2020408.2020426 10.14778/1687553.1687569 10.1145/2736277.2741668 10.1002/widm.1173 10.1145/2433396.2433459 10.1145/2500489 10.1016/j.neucom.2014.04.078 10.1109/MCI.2014.2326099 10.1016/j.neucom.2015.12.042 10.1145/2699026.2699136 10.1111/exsy.12019 10.1126/science.aab3050 10.1016/j.neucom.2013.09.055 10.1109/ICDE.2011.5767930 10.1109/JSTARS.2015.2458855 10.1145/2783258.2789989 10.14778/2212351.2212354 10.1186/s40537-015-0032-1 10.1145/1273496.1273592 10.1186/s40537-015-0030-3 10.1016/j.inffus.2015.03.001 10.1007/978-3-7908-2604-3_16 |
| ContentType | Journal Article |
| Copyright | 2017 Elsevier B.V. |
| Copyright_xml | – notice: 2017 Elsevier B.V. |
| DBID | AAYXX CITATION ADTPV AOWAS |
| DOI | 10.1016/j.neucom.2017.01.026 |
| DatabaseName | CrossRef SwePub SwePub Articles |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 361 |
| ExternalDocumentID | oai_DiVA_org_ltu_61412 10_1016_j_neucom_2017_01_026 S0925231217300577 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD ADTPV AOWAS |
| ID | FETCH-LOGICAL-c437t-5dc3faca10e02128a0e75d107e602454336a13d8bfb3b2992e66f0ad8b587ff63 |
| ISICitedReferencesCount | 712 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000397356700032&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0925-2312 1872-8286 |
| IngestDate | Sat Oct 25 06:23:52 EDT 2025 Tue Nov 18 21:16:28 EST 2025 Sat Nov 29 07:08:30 EST 2025 Fri Feb 23 02:30:24 EST 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Evaluation Big data Data preprocessing Parallelization Machine learning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c437t-5dc3faca10e02128a0e75d107e602454336a13d8bfb3b2992e66f0ad8b587ff63 |
| OpenAccessLink | https://www.sciencedirect.com/science/article/am/pii/S0925231217300577?via%3Dihub |
| PageCount | 12 |
| ParticipantIDs | swepub_primary_oai_DiVA_org_ltu_61412 crossref_primary_10_1016_j_neucom_2017_01_026 crossref_citationtrail_10_1016_j_neucom_2017_01_026 elsevier_sciencedirect_doi_10_1016_j_neucom_2017_01_026 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-05-10 |
| PublicationDateYYYYMMDD | 2017-05-10 |
| PublicationDate_xml | – month: 05 year: 2017 text: 2017-05-10 day: 10 |
| PublicationDecade | 2010 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2017 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | L.Bagheri, H.Goote, A.Hasan, G.Hazard, Risk adjustment of patient expenditures: A big data analytics approach, in Proceedings of the 2013 IEEE International Conference on Big Data, 2013. R. Socher, A. Perelygin, J. Wu, J. Chuang, C. Manning, A. Ng, et al., Recursive deep models for semantic compositionality over a sentiment treebank, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013. R. Collobert, K. Kavukcuoglu, and C. Farabet, Torch7: A Matlab-like Environment for Machine Learning, in: Proceedings of the Neural Information Processing Systems (NIPS) Workshop on BigLearn, 2011. G.De Francisci Morales, SAMOA: a platform for mining big data streams, in: Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 777–778. Su, Agrawal, Woodring, Myers, Wendelberger, Ahrens (bib24) 2014; 17 Wang, Crammer, Vucetic (bib103) 2012; 13 Russell, Norvig (bib5) 2010 Zeng, Wang, Zhang, Liu, Alsaadi (bib84) 2016; 8 Peteiro-Barral, Guijarro-Berdiñas (bib69) 2013; 2 Zhang, Ou, Huang, Wang (bib21) 2015; 2 S. Ramírez-Gallego, S. García, H. Mouriño-Talín, D. Martínez-Rego, V. Bolón-Canedo, A. Alonso-Betanzos, et al., "Data discretization: taxonomy and big data challenge," Wiley Interdisciplinary Reviews, Data Mining and Knowledge Discovery, vol. 6, pp. 5-21, 2016. Goodfellow, Bengio, Courville (bib86) 2016 Amershi, Cakmak, Knox, Kulesza (bib8) 2014; 35 J.J.Pfeiffer , III, J.Neville, P.N.Bennett, Overcoming relational learning biases to accurately predict preferences in large scale networks, in: Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 853–863. Lake, Salakhutdinov, Tenenbaum (bib20) 2015; 350 Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J.Long, R.Girshick, et al., Caffe: Convolutional Architecture for Fast Feature Embedding, in: Proceedings of the 22nd ACM international conference on Multimedia, Orlando, Florida, USA, 2014. Q.Yang, Big data, lifelong machine learning and transfer learning, in: Proceedings of the sixth ACM international conference on Web search and data mining, 2013, pp. 505–506. Popescu, Balmin, Ercegovac, Ailamaki (bib96) 2013; 6 Landset, Khoshgoftaar, Richter, Hasanin (bib63) 2015; 2 Y. LeCun, K. Kavukcuoglu, and C. Farabet, Convolutional networks and applications in vision, in: Proceedings of IEEE International Symposium on Circuits and Systems, 2010, pp. 253–256. R.Gemulla, E.Nijkamp, P.J.Haas, Y.Sismanis, Large-scale matrix factorization with distributed stochastic gradient descent, in: Proceedings of the 17th ACM SIGKDD international conference ion Knowledge discovery and data mining, San Diego, California, USA, 2011, pp. 69–77. (bib67) 2012 Kashyap, Ahmed, Hoque, Roy, Bhattacharyya (bib98) 2015 Vincent, Larochelle, Lajoie, Bengio, Manzagol (bib32) 2010; 11 A. Kumar, A. Beutel, Q. Ho, E.P. Xing, Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data, in: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, Iceland, 2014, pp. 531–539. J. Cervantes, X. Li, W. Yu, Support vector machine classification based on fuzzy clustering for large data sets, in: Proceedings of the 5th MICAI, 2015, pp. 572–582. Chen, Zobel, Verspoor (bib11) 2015 Chen, Lin (bib89) 2014; 2 Rakthanmanon, Campana, Mueen, Batista, Westover, Zhu (bib12) 2013; 7 B.Thuraisingham, Big Data Security and Privacy, in: Proceedings of the 5th ACM Conference on Data and Application Security and Privacy, San Antonio, Texas, USA, 2015. J.Xu, C.Tekin, M.van der Schaar, Learning optimal classifier chains for real-time big data mining, in Proceedings 51st Annu. Allerton Liou, Cheng, Liou, Liou (bib33) 2014; 139 Bengio, LeCun (bib35) 2007 Jiang, Pang, Li, Pan (bib81) 2016; 185 K. Xu, H. Yue, L. Guo, Y. Guo, Y. Fang, Privacy-preserving machine learning algorithms for big data systems, in: Proceedings of the 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS), 2015, pp. 318–327. Singh, Reddy (bib106) 2014; 2 Dekel (bib7) 2008 Deng, Li, Do, Su, Fei-Fei (bib79) 2009; 1 Hsu, Karampatziakis, Langford, Smola (bib65) 2011 Z.Zhao, H.Liu, Spectral feature selection for supervised and unsupervised learning, in: Proceedings of the 24th international conference on Machine learning, 2007, pp. 1151–1157. Tong (bib109) 2010; 2016 Triguero, Peralta, Bacardit, García, Herrera (bib62) 2015; 150 Tsai, Lai, Chao, Vasilakos (bib2) 2015; 2 Lu, Hoi, Wang, Zhao, Liu (bib102) 2016; 17 Chu, Kim, Lin, Yu, Bradski, Ng (bib49) 2006 onference Comm., Control and Comput. (Allerton'13), 2013. Mahajan, Park, Amaro, Sharma, Yazdanbakhsh, Kim (bib91) 2016 T.Mikolov, I.Sutskever, K.Chen, G.S.Corrado, J.Dean, Distributed Representations of Words and Phrases and their Compositionality, presented at the NIPS, Stateline, NV, 2013. Yue, Wu, Fu, Xu, Yin, Liu (bib45) 2017; 219 J. Suzuki, H. Isozaki, and M. Nagata, Learning condensed feature representations from large unsupervised data sets for supervised learning, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Human Language Technologies, short papers, 2, 2011, pp. 636–641. Bengio, Courville, Vincent (bib6) 2013; 35 Nguyen-Dinh, Rossi, Blanke, Tröster (bib19) 2013 Azar, Hassanien (bib31) 2015; 19 T.Xiao, J.Zhang, K.Yang, Y.Peng, Z.Zhang, Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification, in: Proceedings of the ACM International Conference on Multimedia, 2014, pp. 177–186. You, Fu, Song, Randles, Kerbyson, Marquez (bib37) 2015; 76 Zhai, Ong, Tsang (bib104) 2014; 9 Çatak (bib72) 2015 Krizhevsky, Sutskever, Hinton (bib77) 2012 Jordan, Mitchell (bib1) 2015; 349 Zhou, Chen, Wang (bib83) 2013; 120 J.S.Yoo, D.Boulware, D.Kimmey, A Parallel Spatial Co-location Mining Algorithm Based on MapReduce, in: proceedings of the 2014 IEEE International Congress on Big Data, 3rd, pp. 25–31. Collobert, Sinz, Weston, Bottou (bib34) 2006 Borkar, Bu, Carey, Rosen, Polyzotis, Condie (bib51) 2012; 35 Low, Bickson, Gonzalez, Guestrin, Kyrola, Hellerstein (bib52) 2012; 5 C.Dijun Luo, Ding, H.Huang, Parallelization with ultiplicative algorithms for big data mining, in: Proceedings of the 12th International Conference on Data Mining (ICDM), 2012, pp. 489–498. Markl (bib108) 2014; 7 Armes, M (bib110) 2013 J.Zhu, J.Chen, W.Hu, Big Learning with Bayesian Methods. Available Y.Z.Y.-M.Cheung, Discretizing Numerical Attributes in Decision Tree for Big Data Analysis, in: Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW), 2014. A.K.Ghoting, R.E.Pednault, B.Reinwald, V.Sindhwani, S.Tatikonda, Y.Tian, et al., SystemML: Declarative machine learning on MapReduce, in: Proceedings of the 27th International Conference on Data Engineering (ICDE), 2011. Theano Development Team, Theano: A Python framework for fast computation of mathematical expression. Available: arXiv:1605.02688. Japkowicz, Shah (bib4) 2011 Wang, Tang, Nguyen, Altintas (bib44) 2014 Mirchevska, Luštrek, Gams (bib9) 2014; 31 Tan, Tsang, Wang (bib27) 2014; 15 J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, et al., Large scale distributed deep networks, in: Proceedings of the Neural Information Processing Systems, Lake Tahoe, Nevada, United States, 2012, pp. 1232–1240. Yui, Kojima (bib71) 2013 Bolón-Canedo, Sánchez-Maroño, Alonso-Betanzos (bib25) 2015; 30 Owen, Anil, Dunning, Friedman (bib48) 2011 Najafabadi, Villanustre, Khoshgoftaar, Seliya, Wald, Muharemagic (bib3) 2015; 2 X.Cai, F.Nie, H.Huang, Multi-view K-means clustering on big data, in: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013, pp. 2598–2604. 2014. Cavallaro, Riedel, Richerzhagen, Benediktsson, Plaza (bib74) 2015; 8 K.L.C.Zhu, M.Savvides, Distributed class dependent feature analysis — A big data approach, in: proceedings of the 2014 IEEE International Conference on Big Data, 2014. R.Raina, A.Battle, H.Lee, B.Packer, A.Y.Ng, Self-taught learning: transfer learning from unlabeled data, in: Proceedings of the 24th international conference on Machine learning, Corvalis, Oregon, USA, 2007. Vaidya, Yu, Jiang (bib95) 2008; 14 Q.V.Le, J.Ngiam, A.Coates, A.Lahiri, B.Prochnow, A.Y.Ng, On optimization methods for deep learning, in: Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA, 2011. M.Zaharia, M.Chowdhury, M.J.Franklin, S.Shenker, I.Stoica, Spark: cluster computing with working sets, presented at in: Proceedings of the 2nd USENIX conference on Hot topics in Cloud Computing, Boston, MA, 2010. L.Cao, M.Wei, D.Yang, E.A.Rundensteiner, Online outlier exploration over large datasets, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 89–98. Yu (bib10) 2007 W. Xu, Towards Optimal one pass large scale learning with averaged stochastic gradient descent, 2011. Available at: arXiv:1107.2490. E.Bortnikov, A.Frank, E.Hillel, S.Rao, Predicting execution bottlenecks in map-reduce clusters, in: Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing, 2012, pp. 18–18. L. Bottou, Large-Scale Machine Learning with Stochastic Gradient Descent, in: Proceedings of COMPSTAT, 2010, pp. 177–186. Xing, Ho, Dai, Kim, Wei, Lee (bib39) 2015 B.Nelson, T.Olovsson, Security and Privacy for Big Data: A Systematic Literature Review, in: Proceedings of the 2016 IEEE International Conference on Big Data, Washington, D.C, 2016, pp. 3693–3702. T.Kraska, A.Talwalkar, J.Duchi, R.Griffith, M.J.Franklin, M.I.Jordan, MLbase: A Distributed Machine-learning System, in: Proceedings of the 6th Biennial Conference on Innovative Data Systems Research, Asilomar, California, USA, 2013. Gandomi, Haider (bib15) 2015; 35 Chen, Luo, Liu, Zhang, He, Wang (bib90) 2014 M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems," CoRR, 2016. Dong, Krzyzak, Suen (bib55) 2005; 27 Sankar, Karau (bib47) 2015 Mason, Traoré, Lu (10.1016/j.neucom.2017.01.026_bib102) 2016; 17 Vaidya (10.1016/j.neucom.2017.01.026_bib95) 2008; 14 Guo (10.1016/j.neucom.2017.01.026_bib80) 2016; 187 10.1016/j.neucom.2017.01.026_bib100 Jordan (10.1016/j.neucom.2017.01.026_bib1) 2015; 349 10.1016/j.neucom.2017.01.026_bib99 Liou (10.1016/j.neucom.2017.01.026_bib33) 2014; 139 10.1016/j.neucom.2017.01.026_bib101 10.1016/j.neucom.2017.01.026_bib107 10.1016/j.neucom.2017.01.026_bib16 Chen (10.1016/j.neucom.2017.01.026_bib89) 2014; 2 10.1016/j.neucom.2017.01.026_bib13 Bengio (10.1016/j.neucom.2017.01.026_bib35) 2007 10.1016/j.neucom.2017.01.026_bib105 10.1016/j.neucom.2017.01.026_bib14 Collobert (10.1016/j.neucom.2017.01.026_bib34) 2006 Rakthanmanon (10.1016/j.neucom.2017.01.026_bib12) 2013; 7 10.1016/j.neucom.2017.01.026_bib17 Japkowicz (10.1016/j.neucom.2017.01.026_bib4) 2011 10.1016/j.neucom.2017.01.026_bib18 Yu (10.1016/j.neucom.2017.01.026_bib10) 2007 Goodfellow (10.1016/j.neucom.2017.01.026_bib86) 2016 Kashyap (10.1016/j.neucom.2017.01.026_bib98) 2015 Najafabadi (10.1016/j.neucom.2017.01.026_bib3) 2015; 2 Parker (10.1016/j.neucom.2017.01.026_bib68) 2012 10.1016/j.neucom.2017.01.026_bib30 10.1016/j.neucom.2017.01.026_bib111 10.1016/j.neucom.2017.01.026_bib22 Zeng (10.1016/j.neucom.2017.01.026_bib84) 2016; 8 Triguero (10.1016/j.neucom.2017.01.026_bib62) 2015; 150 10.1016/j.neucom.2017.01.026_bib112 Yui (10.1016/j.neucom.2017.01.026_bib71) 2013 Markl (10.1016/j.neucom.2017.01.026_bib108) 2014; 7 Mahajan (10.1016/j.neucom.2017.01.026_bib91) 2016 Sun (10.1016/j.neucom.2017.01.026_bib26) 2015; 26 Dekel (10.1016/j.neucom.2017.01.026_bib7) 2008 10.1016/j.neucom.2017.01.026_bib28 Popescu (10.1016/j.neucom.2017.01.026_bib96) 2013; 6 10.1016/j.neucom.2017.01.026_bib29 Breiman (10.1016/j.neucom.2017.01.026_bib97) 1999; 36 Ganjisaffar (10.1016/j.neucom.2017.01.026_bib59) 2011 Azar (10.1016/j.neucom.2017.01.026_bib31) 2015; 19 Lake (10.1016/j.neucom.2017.01.026_bib20) 2015; 350 10.1016/j.neucom.2017.01.026_bib40 10.1016/j.neucom.2017.01.026_bib41 Zhou (10.1016/j.neucom.2017.01.026_bib83) 2013; 120 Panda (10.1016/j.neucom.2017.01.026_bib38) 2009; 2 Nguyen-Dinh (10.1016/j.neucom.2017.01.026_bib19) 2013 Owen (10.1016/j.neucom.2017.01.026_bib48) 2011 10.1016/j.neucom.2017.01.026_bib36 Hsu (10.1016/j.neucom.2017.01.026_bib65) 2011 Amershi (10.1016/j.neucom.2017.01.026_bib8) 2014; 35 Tan (10.1016/j.neucom.2017.01.026_bib27) 2014; 15 Landset (10.1016/j.neucom.2017.01.026_bib63) 2015; 2 Vincent (10.1016/j.neucom.2017.01.026_bib32) 2010; 11 Mozafari (10.1016/j.neucom.2017.01.026_bib23) 2014; 8 Cavallaro (10.1016/j.neucom.2017.01.026_bib74) 2015; 8 10.1016/j.neucom.2017.01.026_bib50 10.1016/j.neucom.2017.01.026_bib42 10.1016/j.neucom.2017.01.026_bib43 Chu (10.1016/j.neucom.2017.01.026_bib49) 2006 10.1016/j.neucom.2017.01.026_bib46 Jiang (10.1016/j.neucom.2017.01.026_bib81) 2016; 185 Krizhevsky (10.1016/j.neucom.2017.01.026_bib77) 2012 Gandomi (10.1016/j.neucom.2017.01.026_bib15) 2015; 35 Peteiro-Barral (10.1016/j.neucom.2017.01.026_bib69) 2013; 2 Erhan (10.1016/j.neucom.2017.01.026_bib87) 2010; 11 Dong (10.1016/j.neucom.2017.01.026_bib55) 2005; 27 Chen (10.1016/j.neucom.2017.01.026_bib90) 2014 10.1016/j.neucom.2017.01.026_bib60 10.1016/j.neucom.2017.01.026_bib61 10.1016/j.neucom.2017.01.026_bib56 10.1016/j.neucom.2017.01.026_bib53 10.1016/j.neucom.2017.01.026_bib54 10.1016/j.neucom.2017.01.026_bib58 Sankar (10.1016/j.neucom.2017.01.026_bib47) 2015 Low (10.1016/j.neucom.2017.01.026_bib52) 2012; 5 Wang (10.1016/j.neucom.2017.01.026_bib44) 2014 Çatak (10.1016/j.neucom.2017.01.026_bib72) 2015 Xing (10.1016/j.neucom.2017.01.026_bib39) 2015 10.1016/j.neucom.2017.01.026_bib70 10.1016/j.neucom.2017.01.026_bib73 10.1016/j.neucom.2017.01.026_bib66 10.1016/j.neucom.2017.01.026_bib64 (10.1016/j.neucom.2017.01.026_bib67) 2012 Wang (10.1016/j.neucom.2017.01.026_bib103) 2012; 13 Tong (10.1016/j.neucom.2017.01.026_bib109) 2010; 2016 Zhang (10.1016/j.neucom.2017.01.026_bib21) 2015; 2 Tsai (10.1016/j.neucom.2017.01.026_bib2) 2015; 2 Mason (10.1016/j.neucom.2017.01.026_bib57) 2016 Deng (10.1016/j.neucom.2017.01.026_bib79) 2009; 1 Zhai (10.1016/j.neucom.2017.01.026_bib104) 2014; 9 Singh (10.1016/j.neucom.2017.01.026_bib106) 2014; 2 Yue (10.1016/j.neucom.2017.01.026_bib45) 2017; 219 Mirchevska (10.1016/j.neucom.2017.01.026_bib9) 2014; 31 Su (10.1016/j.neucom.2017.01.026_bib24) 2014; 17 10.1016/j.neucom.2017.01.026_bib85 10.1016/j.neucom.2017.01.026_bib82 Armes, M (10.1016/j.neucom.2017.01.026_bib110) 2013 10.1016/j.neucom.2017.01.026_bib78 Russell (10.1016/j.neucom.2017.01.026_bib5) 2010 10.1016/j.neucom.2017.01.026_bib75 10.1016/j.neucom.2017.01.026_bib76 Bolón-Canedo (10.1016/j.neucom.2017.01.026_bib25) 2015; 30 You (10.1016/j.neucom.2017.01.026_bib37) 2015; 76 10.1016/j.neucom.2017.01.026_bib92 10.1016/j.neucom.2017.01.026_bib93 10.1016/j.neucom.2017.01.026_bib94 10.1016/j.neucom.2017.01.026_bib88 Borkar (10.1016/j.neucom.2017.01.026_bib51) 2012; 35 Bengio (10.1016/j.neucom.2017.01.026_bib6) 2013; 35 Chen (10.1016/j.neucom.2017.01.026_bib11) 2015 |
| References_xml | – volume: 2 start-page: 1 year: 2013 end-page: 11 ident: bib69 article-title: A survey of methods for distributed machine learning publication-title: Prog. Artif. Intell. – volume: 11 start-page: 625 year: 2010 end-page: 660 ident: bib87 article-title: Why does Unsupervised Pre-training help deep learning? publication-title: T – reference: G.De Francisci Morales, SAMOA: a platform for mining big data streams, in: Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 777–778. – reference: Y.Z.Y.-M.Cheung, Discretizing Numerical Attributes in Decision Tree for Big Data Analysis, in: Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW), 2014. – year: 2007 ident: bib35 article-title: Scaling learning algorithms towards, AI publication-title: Large Scale Kernel Machines – volume: 26 start-page: 36 year: 2015 end-page: 48 ident: bib26 article-title: A review of Nyström methods for large-scale machine learning publication-title: Inf. Fusion – start-page: 14 year: 2016 end-page: 26 ident: bib91 article-title: TABLA: a unified template-based framework for accelerating statistical machine learning publication-title: I – volume: 2016 year: 2010 ident: bib109 publication-title: Lessons learned developing a practical large scale machine learning system – year: 2016 ident: bib57 article-title: Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force – reference: P.Domingos, G.Hulten, A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering, presented at Proceedings of the Eighteenth International Conference on Machine Learning, 2001, pp. 106–113. – reference: B.Thuraisingham, Big Data Security and Privacy, in: Proceedings of the 5th ACM Conference on Data and Application Security and Privacy, San Antonio, Texas, USA, 2015. – reference: O. Y. S. Al-Jarrah, A., M. Elsalamouny, P. D. Yoo, S. Muhaidat, and K. Kim, Machine-Learning-Based Feature Selection Techniques for Large-Scale Network Intrusion Detection, in: Proceedings of the 2014 IEEE 34th International Conference on in Distributed Computing Systems Workshops (ICDCSW). – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib32 article-title: Stacked denoising Autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 76 start-page: 16 year: 2015 end-page: 31 ident: bib37 article-title: Scaling support vector machines on modern HPC platforms publication-title: J – volume: 2 start-page: 1 year: 2015 end-page: 36 ident: bib63 article-title: A survey of open source tools for machine learning with big data in the Hadoop ecosystem publication-title: J. Big Data – reference: R. Collobert, K. Kavukcuoglu, and C. Farabet, Torch7: A Matlab-like Environment for Machine Learning, in: Proceedings of the Neural Information Processing Systems (NIPS) Workshop on BigLearn, 2011. – start-page: 2 year: 2011 ident: bib59 article-title: Distributed tuning of machine learning algorithms using MapReduce Clusters publication-title: Proc. Third Workshop Large Scale Data Min.: Theory Appl. – volume: 15 start-page: 1371 year: 2014 end-page: 1429 ident: bib27 article-title: Towards ultrahigh dimensional feature selection for big data publication-title: J. Mach. Learn. Res – start-page: 1 year: 2013 end-page: 8 ident: bib71 article-title: A database-Hadoop hybrid approach to Scalable machine learning publication-title: IEEE Int. Congr. Big Data (BigData Congr.) – year: 2015 ident: bib98 publication-title: Big Data Anal. Bioinforma.: A Mach. Learn. Perspect. – start-page: 1 year: 2012 end-page: 6 ident: bib68 article-title: Unexpected challenges in large scale machine learning publication-title: Proc. 1st Int. Workshop Big Data, Streams Heterog. Source Min.: Algorithms, Syst., Program. Models Appl. – start-page: 16 year: 2014 end-page: 25 ident: bib44 article-title: A Scalable data Science workflow approach for Big data Bayesian network learning publication-title: Proc. 2014 IEEE/ACM Int. Symp. Big Data Comput. – reference: L.Bagheri, H.Goote, A.Hasan, G.Hazard, Risk adjustment of patient expenditures: A big data analytics approach, in Proceedings of the 2013 IEEE International Conference on Big Data, 2013. – reference: Theano Development Team, Theano: A Python framework for fast computation of mathematical expression. Available: arXiv:1605.02688. – reference: L. Bottou, Large-Scale Machine Learning with Stochastic Gradient Descent, in: Proceedings of COMPSTAT, 2010, pp. 177–186. – reference: T.Xiao, J.Zhang, K.Yang, Y.Peng, Z.Zhang, Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification, in: Proceedings of the ACM International Conference on Multimedia, 2014, pp. 177–186. – year: 2012 ident: bib67 publication-title: Scaling up Machine Learning: Parallel and Distributed Approaches – reference: X.Cai, F.Nie, H.Huang, Multi-view K-means clustering on big data, in: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, 2013, pp. 2598–2604. – reference: M. Hefeeda, F. Gao, and W. Abd-Almageed, Distributed approximate spectral clustering for large-scale datasets, in: Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing, 2012, pp. 223–234. – volume: 2 start-page: 98 year: 2015 end-page: 110 ident: bib21 article-title: Semi-supervised learning methods for large scale healthcare data analysis publication-title: Int. J. Comput. Healthc. – year: 2011 ident: bib4 article-title: Evaluating Learning Algorithms: a Classification Perspective – volume: 35 start-page: 24 year: 2012 end-page: 32 ident: bib51 article-title: Declarative systems for large-scale machine learning publication-title: I – reference: C.Dijun Luo, Ding, H.Huang, Parallelization with ultiplicative algorithms for big data mining, in: Proceedings of the 12th International Conference on Data Mining (ICDM), 2012, pp. 489–498. – start-page: 201 year: 2006 end-page: 208 ident: bib34 article-title: Trading convexity for scalability publication-title: Proc. 23rd Int. Conf. Mach. Learn. – volume: 7 start-page: 1730 year: 2014 end-page: 1733 ident: bib108 article-title: Breaking the chains: on declarative data analysis and data independence in the big data era publication-title: Proc. VLDB Endow. – reference: Y. LeCun, K. Kavukcuoglu, and C. Farabet, Convolutional networks and applications in vision, in: Proceedings of IEEE International Symposium on Circuits and Systems, 2010, pp. 253–256. – reference: Z.Zhao, H.Liu, Spectral feature selection for supervised and unsupervised learning, in: Proceedings of the 24th international conference on Machine learning, 2007, pp. 1151–1157. – volume: 150 start-page: 331 year: 2015 end-page: 345 ident: bib62 article-title: MRPR: A MapReduce solution for prototype reduction in big data classification publication-title: Neurocomputing – reference: J.S.Yoo, D.Boulware, D.Kimmey, A Parallel Spatial Co-location Mining Algorithm Based on MapReduce, in: proceedings of the 2014 IEEE International Congress on Big Data, 3rd, pp. 25–31. – volume: 350 start-page: 1332 year: 2015 end-page: 1338 ident: bib20 article-title: Human-level concept learning through probabilistic program induction publication-title: Science – volume: 36 start-page: 85 year: 1999 end-page: 103 ident: bib97 article-title: Pasting small votes for classification in large databases and On-Line publication-title: Machine Learn – volume: 19 start-page: 1115 year: 2015 end-page: 1127 ident: bib31 article-title: Dimensionality reduction of medical big data using neural-fuzzy classifier publication-title: Soft Comput. - A Fusion Found., Methodol. Appl. – volume: 9 start-page: 14 year: 2014 end-page: 26 ident: bib104 article-title: The emerging big dimensionality publication-title: IEEE Comput. Intell. Mag. – volume: 6 start-page: 1678 year: 2013 end-page: 1689 ident: bib96 article-title: PREDIcT: towards predicting the runtime of large scale iterative analytics publication-title: Proc. VLDB Endow. – year: 2013 ident: bib110 article-title: Using Big data and predictive machine learning in aerospace test environments publication-title: IEEE Autotestcon – start-page: 1 year: 2015 end-page: 13 ident: bib72 article-title: Classification with boosting of extreme learning machine over arbitrarily partitioned data publication-title: Soft Comput. – year: 2012 ident: bib77 publication-title: Imagen. Classif. Deep convolutional Neural Netw. – volume: 1 year: 2009 ident: bib79 article-title: Construction and analysis of a large scale image ontology publication-title: Vis. Sci. Soc. – volume: 2 start-page: 1426 year: 2009 end-page: 1437 ident: bib38 article-title: PLANET: massively parallel learning of tree ensembles with MapReduce publication-title: Proc. VLDB Endow. – volume: 349 start-page: 255 year: 2015 end-page: 260 ident: bib1 article-title: Machine learning: trends, perspectives, and prospects publication-title: Science – volume: 2 start-page: 514 year: 2014 end-page: 525 ident: bib89 article-title: Big data deep learning: challenges and perspectives publication-title: Access, IEEE – volume: 2 start-page: 1 year: 2015 end-page: 32 ident: bib2 article-title: Big data analytics: a survey publication-title: J. Big Data – volume: 8 start-page: 125 year: 2014 end-page: 136 ident: bib23 article-title: Scaling up crowd-sourcing to very large datasets: a case for active learning publication-title: Proc. VLDB Endow. – volume: 27 start-page: 603 year: 2005 end-page: 618 ident: bib55 article-title: Fast SVM training algorithm with decomposition on very large data sets publication-title: I – start-page: 49 year: 2015 end-page: 67 ident: bib39 article-title: Petuum: a new platform for distributed machine learning on Big data publication-title: IEEE Trans. Big Data – reference: T.Kraska, A.Talwalkar, J.Duchi, R.Griffith, M.J.Franklin, M.I.Jordan, MLbase: A Distributed Machine-learning System, in: Proceedings of the 6th Biennial Conference on Innovative Data Systems Research, Asilomar, California, USA, 2013. – volume: 2 start-page: 1 year: 2014 end-page: 20 ident: bib106 article-title: A survey on platforms for big data analytics publication-title: J. Big Data – volume: 35 start-page: 105 year: 2014 end-page: 120 ident: bib8 article-title: Power to the people: the role of humans in Interactive machine learning publication-title: AI Mag. – volume: 17 start-page: 1081 year: 2014 end-page: 1100 ident: bib24 article-title: Effective and efficient data sampling using bitmap indices publication-title: Clust. Comput. – reference: M.Zaharia, M.Chowdhury, M.J.Franklin, S.Shenker, I.Stoica, Spark: cluster computing with working sets, presented at in: Proceedings of the 2nd USENIX conference on Hot topics in Cloud Computing, Boston, MA, 2010. – reference: K.L.C.Zhu, M.Savvides, Distributed class dependent feature analysis — A big data approach, in: proceedings of the 2014 IEEE International Conference on Big Data, 2014. – volume: 8 start-page: 4634 year: 2015 end-page: 4646 ident: bib74 article-title: On Understanding Big data impacts in remotely sensed image classification using support vector machine methods publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 5 start-page: 716 year: 2012 end-page: 727 ident: bib52 article-title: Distributed GraphLab: a framework for machine learning and data mining in the cloud publication-title: Proc. VLDB Endow. – year: 2010 ident: bib5 publication-title: Artificial Intelligence: A Modern Approach – reference: J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, et al., Large scale distributed deep networks, in: Proceedings of the Neural Information Processing Systems, Lake Tahoe, Nevada, United States, 2012, pp. 1232–1240. – start-page: 609 year: 2014 end-page: 622 ident: bib90 article-title: DaDianNao: a machine-learning Supercomputer publication-title: 47th Annu. IEEE/ACM Int. Symp. Micro. – reference: W. Xu, Towards Optimal one pass large scale learning with averaged stochastic gradient descent, 2011. Available at: arXiv:1107.2490. – year: 2011 ident: bib48 article-title: Mahout in Action – reference: J.Xu, C.Tekin, M.van der Schaar, Learning optimal classifier chains for real-time big data mining, in Proceedings 51st Annu. Allerton – volume: 8 start-page: 684 year: 2016 end-page: 692 ident: bib84 article-title: Deep belief networks for quantitative analysis of a gold immunochromatographic strip publication-title: Cogn. Comput. – volume: 185 start-page: 163 year: 2016 end-page: 170 ident: bib81 article-title: Speed up deep neural network based pedestrian detection by sharing features across multi-scale models publication-title: Neurocomputing – volume: 13 start-page: 3103 year: 2012 end-page: 3131 ident: bib103 article-title: Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training publication-title: T – volume: 2 start-page: 1 year: 2015 end-page: 21 ident: bib3 article-title: Deep learning applications and challenges in big data analytics publication-title: J. Big Data – reference: Q.Yang, Big data, lifelong machine learning and transfer learning, in: Proceedings of the sixth ACM international conference on Web search and data mining, 2013, pp. 505–506. – volume: 219 start-page: 364 year: 2017 end-page: 375 ident: bib45 article-title: A data-intensive approach for discovering user similarities in social behavioral interactions based on the bayesian network publication-title: Neurocomputing – reference: A.K.Ghoting, R.E.Pednault, B.Reinwald, V.Sindhwani, S.Tatikonda, Y.Tian, et al., SystemML: Declarative machine learning on MapReduce, in: Proceedings of the 27th International Conference on Data Engineering (ICDE), 2011. – reference: R. Socher, A. Perelygin, J. Wu, J. Chuang, C. Manning, A. Ng, et al., Recursive deep models for semantic compositionality over a sentiment treebank, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013. – reference: K. Xu, H. Yue, L. Guo, Y. Guo, Y. Fang, Privacy-preserving machine learning algorithms for big data systems, in: Proceedings of the 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS), 2015, pp. 318–327. – year: 2007 ident: bib10 article-title: Incorporating Prior Domain Knowledge into Inductive Machine Learning publication-title: Computing Sciences – volume: 7 start-page: 10 year: 2013 ident: bib12 article-title: Addressing Big data time series: mining Trillions of time series subsequences Under dynamic time Warping publication-title: ACM Trans. Knowl. Discov. Data – reference: J. Suzuki, H. Isozaki, and M. Nagata, Learning condensed feature representations from large unsupervised data sets for supervised learning, in: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Human Language Technologies, short papers, 2, 2011, pp. 636–641. – volume: 30 start-page: 136 year: 2015 end-page: 150 ident: bib25 article-title: Distributed feature selection publication-title: Appl. Soft Comput. – volume: 35 start-page: 137 year: 2015 end-page: 144 ident: bib15 article-title: Beyond the hype: Big data concepts, methods, and analytics publication-title: Int. J. Inf. Manag. – reference: L.Cao, M.Wei, D.Yang, E.A.Rundensteiner, Online outlier exploration over large datasets, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 89–98. – volume: 14 start-page: 161 year: 2008 end-page: 178 ident: bib95 article-title: Privacy-preserving SVM classification publication-title: Knowledge Inf. Syst. – reference: J.Zhu, J.Chen, W.Hu, Big Learning with Bayesian Methods. Available: – reference: B.Nelson, T.Olovsson, Security and Privacy for Big Data: A Systematic Literature Review, in: Proceedings of the 2016 IEEE International Conference on Big Data, Washington, D.C, 2016, pp. 3693–3702. – year: 2015 ident: bib47 publication-title: Fast Data Processing with Spark – volume: 31 start-page: 163 year: 2014 end-page: 175 ident: bib9 article-title: Combining domain knowledge and machine learning for robust fall detection publication-title: Expert Syst. – reference: S. Ramírez-Gallego, S. García, H. Mouriño-Talín, D. Martínez-Rego, V. Bolón-Canedo, A. Alonso-Betanzos, et al., "Data discretization: taxonomy and big data challenge," Wiley Interdisciplinary Reviews, Data Mining and Knowledge Discovery, vol. 6, pp. 5-21, 2016. – start-page: 377 year: 2008 end-page: 384 ident: bib7 article-title: From Online to Batch Learning with Cutoff-Averaging publication-title: NIPS – reference: E.Bortnikov, A.Frank, E.Hillel, S.Rao, Predicting execution bottlenecks in map-reduce clusters, in: Proceedings of the 4th USENIX conference on Hot Topics in Cloud Ccomputing, 2012, pp. 18–18. – reference: R.Raina, A.Battle, H.Lee, B.Packer, A.Y.Ng, Self-taught learning: transfer learning from unlabeled data, in: Proceedings of the 24th international conference on Machine learning, Corvalis, Oregon, USA, 2007. – start-page: 35 year: 2013 end-page: 38 ident: bib19 article-title: Combining crowd-generated media and personal data: semi-supervised learning for context recognition publication-title: Proc. 1st ACM Int. Workshop Pers. data meets Distrib. Multimed. – reference: T.Mikolov, I.Sutskever, K.Chen, G.S.Corrado, J.Dean, Distributed Representations of Words and Phrases and their Compositionality, presented at the NIPS, Stateline, NV, 2013. – reference: , 2014. – reference: R.Gemulla, E.Nijkamp, P.J.Haas, Y.Sismanis, Large-scale matrix factorization with distributed stochastic gradient descent, in: Proceedings of the 17th ACM SIGKDD international conference ion Knowledge discovery and data mining, San Diego, California, USA, 2011, pp. 69–77. – volume: 35 start-page: 1798 year: 2013 end-page: 1828 ident: bib6 article-title: Representation learning: a review and new perspectives publication-title: IEEE Trans. on Pattern Anal. Mach. Intell., Trans. – volume: 139 start-page: 84 year: 2014 end-page: 96 ident: bib33 article-title: Autoencoder for words publication-title: Neurocomputing – start-page: 4 year: 2015 end-page: 12 ident: bib11 article-title: Evaluation of a machine learning duplicate detection method for bioinformatics Databases publication-title: Proc. ACM Ninth Int. Workshop Data Text. Min. Biomed. Inform. – reference: T.Yang, Q.Lin, R.Jin, Big data analytics: Optimization and randomization, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 2327–2327. – reference: Q.V.Le, J.Ngiam, A.Coates, A.Lahiri, B.Prochnow, A.Y.Ng, On optimization methods for deep learning, in: Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA, 2011. – volume: 120 start-page: 536 year: 2013 end-page: 546 ident: bib83 article-title: Active deep learning method for semi-supervised sentiment classification publication-title: Neurocomputing – start-page: 281 year: 2006 end-page: 288 ident: bib49 article-title: Map-reduce for machine learning on multicore publication-title: N – year: 2016 ident: bib86 article-title: Deep Learning – year: 2011 ident: bib65 article-title: Parallel online learning publication-title: Scaling up machine learning: Parallel and distributed approaches – volume: 187 start-page: 27 year: 2016 end-page: 48 ident: bib80 article-title: Deep learning for visual understanding: a review publication-title: Neurocomputing – reference: M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems," CoRR, 2016. – reference: Y.Jia, E.Shelhamer, J.Donahue, S.Karayev, J.Long, R.Girshick, et al., Caffe: Convolutional Architecture for Fast Feature Embedding, in: Proceedings of the 22nd ACM international conference on Multimedia, Orlando, Florida, USA, 2014. – reference: J.J.Pfeiffer , III, J.Neville, P.N.Bennett, Overcoming relational learning biases to accurately predict preferences in large scale networks, in: Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 853–863. – reference: A. Kumar, A. Beutel, Q. Ho, E.P. Xing, Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data, in: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, Iceland, 2014, pp. 531–539. – volume: 17 start-page: 1 year: 2016 end-page: 43 ident: bib102 article-title: Large scale online kernel learning publication-title: J. Mach. Learn. Res. – reference: J. Cervantes, X. Li, W. Yu, Support vector machine classification based on fuzzy clustering for large data sets, in: Proceedings of the 5th MICAI, 2015, pp. 572–582. – reference: onference Comm., Control and Comput. (Allerton'13), 2013. – ident: 10.1016/j.neucom.2017.01.026_bib53 – ident: 10.1016/j.neucom.2017.01.026_bib105 doi: 10.1145/2647868.2654926 – volume: 2 start-page: 1 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib3 article-title: Deep learning applications and challenges in big data analytics publication-title: J. Big Data doi: 10.1186/s40537-014-0007-7 – volume: 8 start-page: 684 year: 2016 ident: 10.1016/j.neucom.2017.01.026_bib84 article-title: Deep belief networks for quantitative analysis of a gold immunochromatographic strip publication-title: Cogn. Comput. doi: 10.1007/s12559-016-9404-x – ident: 10.1016/j.neucom.2017.01.026_bib76 – ident: 10.1016/j.neucom.2017.01.026_bib78 doi: 10.1109/ISCAS.2010.5537907 – volume: 35 start-page: 24 year: 2012 ident: 10.1016/j.neucom.2017.01.026_bib51 article-title: Declarative systems for large-scale machine learning publication-title: IEEE Data Eng. Bull. – ident: 10.1016/j.neucom.2017.01.026_bib60 doi: 10.1109/ICDM.2012.155 – year: 2016 ident: 10.1016/j.neucom.2017.01.026_bib86 – volume: 14 start-page: 161 year: 2008 ident: 10.1016/j.neucom.2017.01.026_bib95 article-title: Privacy-preserving SVM classification publication-title: Knowledge Inf. Syst. doi: 10.1007/s10115-007-0073-7 – ident: 10.1016/j.neucom.2017.01.026_bib99 – start-page: 609 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib90 article-title: DaDianNao: a machine-learning Supercomputer publication-title: 47th Annu. IEEE/ACM Int. Symp. Micro. – ident: 10.1016/j.neucom.2017.01.026_bib14 doi: 10.1145/2783258.2783387 – ident: 10.1016/j.neucom.2017.01.026_bib73 doi: 10.1145/2287076.2287111 – year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib98 publication-title: Big Data Anal. Bioinforma.: A Mach. Learn. Perspect. – volume: 36 start-page: 85 year: 1999 ident: 10.1016/j.neucom.2017.01.026_bib97 article-title: Pasting small votes for classification in large databases and On-Line publication-title: Machine Learn. doi: 10.1023/A:1007563306331 – volume: 2 start-page: 1 year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib69 article-title: A survey of methods for distributed machine learning publication-title: Prog. Artif. Intell. doi: 10.1007/s13748-012-0035-5 – start-page: 16 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib44 article-title: A Scalable data Science workflow approach for Big data Bayesian network learning publication-title: Proc. 2014 IEEE/ACM Int. Symp. Big Data Comput. doi: 10.1109/BDC.2014.10 – year: 2016 ident: 10.1016/j.neucom.2017.01.026_bib57 – volume: 219 start-page: 364 year: 2017 ident: 10.1016/j.neucom.2017.01.026_bib45 article-title: A data-intensive approach for discovering user similarities in social behavioral interactions based on the bayesian network publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.042 – ident: 10.1016/j.neucom.2017.01.026_bib100 doi: 10.1145/2487788.2488042 – ident: 10.1016/j.neucom.2017.01.026_bib58 – volume: 6 start-page: 1678 year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib96 article-title: PREDIcT: towards predicting the runtime of large scale iterative analytics publication-title: Proc. VLDB Endow. doi: 10.14778/2556549.2556553 – start-page: 35 year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib19 article-title: Combining crowd-generated media and personal data: semi-supervised learning for context recognition publication-title: Proc. 1st ACM Int. Workshop Pers. data meets Distrib. Multimed. doi: 10.1145/2509352.2509396 – start-page: 1 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib72 article-title: Classification with boosting of extreme learning machine over arbitrarily partitioned data publication-title: Soft Comput. – volume: 30 start-page: 136 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib25 article-title: Distributed feature selection publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.01.035 – ident: 10.1016/j.neucom.2017.01.026_bib30 doi: 10.1109/ICDCSW.2014.14 – ident: 10.1016/j.neucom.2017.01.026_bib61 doi: 10.1109/BigData.Congress.2014.14 – ident: 10.1016/j.neucom.2017.01.026_bib28 doi: 10.1145/1273496.1273641 – ident: 10.1016/j.neucom.2017.01.026_bib112 doi: 10.1109/BigData.2016.7841037 – volume: 35 start-page: 1798 year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib6 article-title: Representation learning: a review and new perspectives publication-title: IEEE Trans. on Pattern Anal. Mach. Intell., Trans. doi: 10.1109/TPAMI.2013.50 – ident: 10.1016/j.neucom.2017.01.026_bib22 – ident: 10.1016/j.neucom.2017.01.026_bib54 doi: 10.1145/2647868.2654889 – start-page: 4 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib11 article-title: Evaluation of a machine learning duplicate detection method for bioinformatics Databases publication-title: Proc. ACM Ninth Int. Workshop Data Text. Min. Biomed. Inform. – start-page: 201 year: 2006 ident: 10.1016/j.neucom.2017.01.026_bib34 article-title: Trading convexity for scalability publication-title: Proc. 23rd Int. Conf. Mach. Learn. – ident: 10.1016/j.neucom.2017.01.026_bib94 doi: 10.1109/ICDCS.2015.40 – ident: 10.1016/j.neucom.2017.01.026_bib42 – volume: 35 start-page: 137 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib15 article-title: Beyond the hype: Big data concepts, methods, and analytics publication-title: Int. J. Inf. Manag. – ident: 10.1016/j.neucom.2017.01.026_bib70 – ident: 10.1016/j.neucom.2017.01.026_bib93 – ident: 10.1016/j.neucom.2017.01.026_bib36 – year: 2007 ident: 10.1016/j.neucom.2017.01.026_bib35 article-title: Scaling learning algorithms towards, AI – volume: 120 start-page: 536 year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib83 article-title: Active deep learning method for semi-supervised sentiment classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.04.017 – year: 2012 ident: 10.1016/j.neucom.2017.01.026_bib77 publication-title: Imagen. Classif. Deep convolutional Neural Netw. – volume: 2 start-page: 514 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib89 article-title: Big data deep learning: challenges and perspectives publication-title: Access, IEEE doi: 10.1109/ACCESS.2014.2325029 – volume: 349 start-page: 255 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib1 article-title: Machine learning: trends, perspectives, and prospects publication-title: Science doi: 10.1126/science.aaa8415 – volume: 19 start-page: 1115 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib31 article-title: Dimensionality reduction of medical big data using neural-fuzzy classifier publication-title: Soft Comput. - A Fusion Found., Methodol. Appl. – ident: 10.1016/j.neucom.2017.01.026_bib56 – volume: 17 start-page: 1081 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib24 article-title: Effective and efficient data sampling using bitmap indices publication-title: Clust. Comput. doi: 10.1007/s10586-014-0360-5 – volume: 13 start-page: 3103 year: 2012 ident: 10.1016/j.neucom.2017.01.026_bib103 article-title: Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training publication-title: The J. Mach. Learn. Res. – volume: 76 start-page: 16 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib37 article-title: Scaling support vector machines on modern HPC platforms publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2014.09.005 – year: 2011 ident: 10.1016/j.neucom.2017.01.026_bib4 – ident: 10.1016/j.neucom.2017.01.026_bib29 doi: 10.1007/11925231_54 – volume: 8 start-page: 125 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib23 article-title: Scaling up crowd-sourcing to very large datasets: a case for active learning publication-title: Proc. VLDB Endow. doi: 10.14778/2735471.2735474 – volume: 27 start-page: 603 year: 2005 ident: 10.1016/j.neucom.2017.01.026_bib55 article-title: Fast SVM training algorithm with decomposition on very large data sets publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.77 – year: 2011 ident: 10.1016/j.neucom.2017.01.026_bib65 article-title: Parallel online learning – volume: 2 start-page: 98 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib21 article-title: Semi-supervised learning methods for large scale healthcare data analysis publication-title: Int. J. Comput. Healthc. doi: 10.1504/IJCIH.2015.069788 – volume: 7 start-page: 1730 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib108 article-title: Breaking the chains: on declarative data analysis and data independence in the big data era publication-title: Proc. VLDB Endow. doi: 10.14778/2733004.2733075 – volume: 11 start-page: 625 year: 2010 ident: 10.1016/j.neucom.2017.01.026_bib87 article-title: Why does Unsupervised Pre-training help deep learning? publication-title: The J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2017.01.026_bib82 doi: 10.18653/v1/D13-1170 – volume: 187 start-page: 27 year: 2016 ident: 10.1016/j.neucom.2017.01.026_bib80 article-title: Deep learning for visual understanding: a review publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.09.116 – year: 2007 ident: 10.1016/j.neucom.2017.01.026_bib10 article-title: Incorporating Prior Domain Knowledge into Inductive Machine Learning – start-page: 49 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib39 article-title: Petuum: a new platform for distributed machine learning on Big data publication-title: IEEE Trans. Big Data doi: 10.1109/TBDATA.2015.2472014 – ident: 10.1016/j.neucom.2017.01.026_bib64 doi: 10.1145/2020408.2020426 – volume: 15 start-page: 1371 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib27 article-title: Towards ultrahigh dimensional feature selection for big data publication-title: J. Mach. Learn. Res. – volume: 2 start-page: 1426 year: 2009 ident: 10.1016/j.neucom.2017.01.026_bib38 article-title: PLANET: massively parallel learning of tree ensembles with MapReduce publication-title: Proc. VLDB Endow. doi: 10.14778/1687553.1687569 – ident: 10.1016/j.neucom.2017.01.026_bib40 – start-page: 1 year: 2012 ident: 10.1016/j.neucom.2017.01.026_bib68 article-title: Unexpected challenges in large scale machine learning publication-title: Proc. 1st Int. Workshop Big Data, Streams Heterog. Source Min.: Algorithms, Syst., Program. Models Appl. – ident: 10.1016/j.neucom.2017.01.026_bib13 doi: 10.1145/2736277.2741668 – volume: 35 start-page: 105 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib8 article-title: Power to the people: the role of humans in Interactive machine learning publication-title: AI Mag. – start-page: 377 year: 2008 ident: 10.1016/j.neucom.2017.01.026_bib7 article-title: From Online to Batch Learning with Cutoff-Averaging – ident: 10.1016/j.neucom.2017.01.026_bib17 doi: 10.1002/widm.1173 – ident: 10.1016/j.neucom.2017.01.026_bib75 – ident: 10.1016/j.neucom.2017.01.026_bib101 doi: 10.1145/2433396.2433459 – volume: 17 start-page: 1 year: 2016 ident: 10.1016/j.neucom.2017.01.026_bib102 article-title: Large scale online kernel learning publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 10 year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib12 article-title: Addressing Big data time series: mining Trillions of time series subsequences Under dynamic time Warping publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/2500489 – start-page: 281 year: 2006 ident: 10.1016/j.neucom.2017.01.026_bib49 article-title: Map-reduce for machine learning on multicore publication-title: NIPS – year: 2010 ident: 10.1016/j.neucom.2017.01.026_bib5 – volume: 150 start-page: 331 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib62 article-title: MRPR: A MapReduce solution for prototype reduction in big data classification publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.04.078 – ident: 10.1016/j.neucom.2017.01.026_bib92 – ident: 10.1016/j.neucom.2017.01.026_bib16 – start-page: 2 year: 2011 ident: 10.1016/j.neucom.2017.01.026_bib59 article-title: Distributed tuning of machine learning algorithms using MapReduce Clusters publication-title: Proc. Third Workshop Large Scale Data Min.: Theory Appl. – start-page: 14 year: 2016 ident: 10.1016/j.neucom.2017.01.026_bib91 article-title: TABLA: a unified template-based framework for accelerating statistical machine learning publication-title: IEEE Int. Symp. High. Perform. Comput. Archit. (HPCA) – volume: 11 start-page: 3371 year: 2010 ident: 10.1016/j.neucom.2017.01.026_bib32 article-title: Stacked denoising Autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2017.01.026_bib107 – volume: 9 start-page: 14 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib104 article-title: The emerging big dimensionality publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2014.2326099 – volume: 185 start-page: 163 year: 2016 ident: 10.1016/j.neucom.2017.01.026_bib81 article-title: Speed up deep neural network based pedestrian detection by sharing features across multi-scale models publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.12.042 – ident: 10.1016/j.neucom.2017.01.026_bib111 doi: 10.1145/2699026.2699136 – volume: 31 start-page: 163 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib9 article-title: Combining domain knowledge and machine learning for robust fall detection publication-title: Expert Syst. doi: 10.1111/exsy.12019 – year: 2011 ident: 10.1016/j.neucom.2017.01.026_bib48 – volume: 2 start-page: 1 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib106 article-title: A survey on platforms for big data analytics publication-title: J. Big Data – ident: 10.1016/j.neucom.2017.01.026_bib88 – volume: 350 start-page: 1332 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib20 article-title: Human-level concept learning through probabilistic program induction publication-title: Science doi: 10.1126/science.aab3050 – ident: 10.1016/j.neucom.2017.01.026_bib46 – year: 2012 ident: 10.1016/j.neucom.2017.01.026_bib67 – volume: 139 start-page: 84 year: 2014 ident: 10.1016/j.neucom.2017.01.026_bib33 article-title: Autoencoder for words publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.09.055 – year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib110 article-title: Using Big data and predictive machine learning in aerospace test environments publication-title: IEEE Autotestcon – ident: 10.1016/j.neucom.2017.01.026_bib50 doi: 10.1109/ICDE.2011.5767930 – volume: 1 year: 2009 ident: 10.1016/j.neucom.2017.01.026_bib79 article-title: Construction and analysis of a large scale image ontology publication-title: Vis. Sci. Soc. – volume: 8 start-page: 4634 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib74 article-title: On Understanding Big data impacts in remotely sensed image classification using support vector machine methods publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2015.2458855 – ident: 10.1016/j.neucom.2017.01.026_bib41 doi: 10.1145/2783258.2789989 – volume: 5 start-page: 716 year: 2012 ident: 10.1016/j.neucom.2017.01.026_bib52 article-title: Distributed GraphLab: a framework for machine learning and data mining in the cloud publication-title: Proc. VLDB Endow. doi: 10.14778/2212351.2212354 – volume: 2 start-page: 1 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib63 article-title: A survey of open source tools for machine learning with big data in the Hadoop ecosystem publication-title: J. Big Data doi: 10.1186/s40537-015-0032-1 – start-page: 1 year: 2013 ident: 10.1016/j.neucom.2017.01.026_bib71 article-title: A database-Hadoop hybrid approach to Scalable machine learning publication-title: IEEE Int. Congr. Big Data (BigData Congr.) – year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib47 – ident: 10.1016/j.neucom.2017.01.026_bib85 doi: 10.1145/1273496.1273592 – ident: 10.1016/j.neucom.2017.01.026_bib66 – ident: 10.1016/j.neucom.2017.01.026_bib18 – volume: 2 start-page: 1 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib2 article-title: Big data analytics: a survey publication-title: J. Big Data doi: 10.1186/s40537-015-0030-3 – volume: 2016 year: 2010 ident: 10.1016/j.neucom.2017.01.026_bib109 – volume: 26 start-page: 36 year: 2015 ident: 10.1016/j.neucom.2017.01.026_bib26 article-title: A review of Nyström methods for large-scale machine learning publication-title: Inf. Fusion doi: 10.1016/j.inffus.2015.03.001 – ident: 10.1016/j.neucom.2017.01.026_bib43 doi: 10.1007/978-3-7908-2604-3_16 |
| SSID | ssj0017129 |
| Score | 2.6745894 |
| Snippet | Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to... |
| SourceID | swepub crossref elsevier |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 350 |
| SubjectTerms | Big data Data preprocessing Distribuerade datorsystem Evaluation Machine learning Parallelization Pervasive Mobile Computing |
| Title | Machine learning on big data: Opportunities and challenges |
| URI | https://dx.doi.org/10.1016/j.neucom.2017.01.026 https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-61412 |
| Volume | 237 |
| WOSCitedRecordID | wos000397356700032&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect Freedom Collection - Elsevier customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELag5cCllJdoocgHOFWp4jiv7S2CIkCi9FBWKy6WnTjblG2y2mygP58Z28luKVB64BJFo42TzXwZz4zH3xDySqclh4lNenkBsUkoufKQ1crjXPq-UnHoc2WaTSTHx-lkMjpxywWtaSeQ1HV6eTma_1dVgwyUjVtnb6HuYVAQwDkoHY6gdjj-k-I_mfJI3feDmOJygKqm-1gLivH_5zm63F1tqFTttra-oUq77qoa2o7cNH1w6YTsAlkVCoTQkD74etZ0LrYfDPyJS6qeVRe6WmXsXe0v4PFH10vHsq1m8put9cswjQ-Cpt0fH6ynI2CKM0ym63nFIPLAabxiYgNL7OKMJLdUs26-5ZaM_Zopt1mF84Nad1jXg_eyBKu_Yc7-ZUYb6gz7ErZzYUcROIrwmYBR7pLNIIlGYAk3sw9Hk4_D2lPCAsvQ6P5Iv-HSVAVef5o_OjTrzLPGWzndJlsuzKCZhcdDckfXj8iDvoUHdRb9MTl0aKE9WmhTU0ALRbQc0itYoYAVusLKE_Ll3dHpm_eea6fh5SFPll5U5LyUuWS-Rl7_VPo6iQoI_3WM6-8h57FkvEhVqbgCLyXQcVz6EgRRmpRlzJ-Sjbqp9TNCIUgNJStUEQUylFGkFCsCJX2J9QGsZDuE9-9E5I5rHluezMTfNLJDvOGqueVaueH3Sf-6hfMXrR8oAEM3XPnaame4D7Ksv63GmWgWUzFbdgLcVhbs3vKJnpP7q2_iBdlYLjq9R-7l35dVu3jpgPYT9HaaMQ |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+learning+on+big+data%3A+Opportunities+and+challenges&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Zhou%2C+Lina&rft.au=Pan%2C+Shimei&rft.au=Wang%2C+Jianwu&rft.au=Vasilakos%2C+Athanasios+V.&rft.date=2017-05-10&rft.issn=0925-2312&rft.volume=237&rft.spage=350&rft.epage=361&rft_id=info:doi/10.1016%2Fj.neucom.2017.01.026&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_neucom_2017_01_026 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |