Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data
In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the pro...
Uloženo v:
| Vydáno v: | PloS one Ročník 12; číslo 12; s. e0188746 |
|---|---|
| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Public Library of Science
13.12.2017
Public Library of Science (PLoS) |
| Témata: | |
| ISSN: | 1932-6203, 1932-6203 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. |
|---|---|
| AbstractList | In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks.In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. |
| Audience | Academic |
| Author | Ye, Fei |
| AuthorAffiliation | Beihang University, CHINA School of information science and technology, Southwest Jiaotong University, ChengDu, China |
| AuthorAffiliation_xml | – name: School of information science and technology, Southwest Jiaotong University, ChengDu, China – name: Beihang University, CHINA |
| Author_xml | – sequence: 1 givenname: Fei orcidid: 0000-0002-5894-2178 surname: Ye fullname: Ye, Fei |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29236718$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNk9tu1DAQhiNURA_wBggiISG4yGInGzvhAqmqOFSqVMTp1prYk10XJ05thwIPwvPibNOqW1UI5WKi8Tf_2L_H-8lOb3tMkseULGjB6aszO7oezGKI6QWhVcWX7F6yR-siz1hOip0b_7vJvvdnhJRFxdiDZDev84JxWu0lfz6CC1oaTP0FuC61Q9Cd_g1B2z5rwKNKYQy2iwmZDuCgw4Au9WhQTkzaWpcqxCHtcXRgYggX1n33KfQq1SHGYTBabgR9qvvUgFth5iXEnhOz1qt1pnSHvY9IVFAQ4GFyvwXj8dEcD5Kv795-OfqQnZy-Pz46PMkky_OQVRCPTSgCctLIuqEVFkUtKWJcqXOOlLUosclL4LRQUqmyIbSBkiFVrOTFQfL0Uncw1ovZUi9ozdmSElKzSBxfEsrCmRic7sD9Eha02CSsW4nZQQGUo-RQk5zgkkjSsJYyRaMOVAUlVdR6M3cbmw6VxD5Ey7ZEt1d6vRYr-0OUPK9KMm33xSzg7PmIPohOe4nGQI923Oyb57QqyzKiz26hd59uplbxOoTuWxv7yklUHJa0YnXJ2aS1uIOKn8JOyzh_rY75rYKXWwWRCfgzrGD0Xhx__vT_7Om3bfb5DXaNYMLaWzNuhmsbfHLT6WuLrwY_AstLQDrrvcP2GqFETO_ryi4xvS8xv69Y9vpWmdRhM9vREW3-XfwXjfQu7Q |
| CitedBy_id | crossref_primary_10_1080_08839514_2021_1972251 crossref_primary_10_1016_j_isci_2024_109148 crossref_primary_10_1109_ACCESS_2019_2904709 crossref_primary_10_1080_10106049_2021_1878291 crossref_primary_10_1109_TNNLS_2021_3130896 crossref_primary_10_1007_s41060_024_00690_y crossref_primary_10_1007_s13369_023_08021_2 crossref_primary_10_1109_ACCESS_2019_2903015 crossref_primary_10_1109_ACCESS_2019_2909756 crossref_primary_10_3390_math11091989 crossref_primary_10_1007_s10462_019_09719_2 crossref_primary_10_1016_j_comnet_2019_107042 crossref_primary_10_1016_j_knosys_2019_01_015 crossref_primary_10_1155_2019_8213056 crossref_primary_10_1002_ima_22562 crossref_primary_10_3233_JIFS_179211 crossref_primary_10_1002_ima_22522 crossref_primary_10_1109_TNNLS_2020_3016666 crossref_primary_10_1016_j_energy_2025_138078 crossref_primary_10_1016_j_oceaneng_2024_118398 crossref_primary_10_2478_jaiscr_2024_0015 crossref_primary_10_1109_TNNLS_2021_3100554 crossref_primary_10_32604_cmc_2022_020485 crossref_primary_10_1007_s11063_022_11055_6 crossref_primary_10_1016_j_applthermaleng_2022_119917 crossref_primary_10_1016_j_eswa_2023_121044 crossref_primary_10_1007_s00521_021_05960_5 crossref_primary_10_1007_s00521_018_3653_4 crossref_primary_10_1155_2022_1128217 crossref_primary_10_1016_j_cie_2021_107400 crossref_primary_10_1109_ACCESS_2025_3591403 crossref_primary_10_1007_s40996_025_02016_9 crossref_primary_10_1017_S1759078721001690 crossref_primary_10_1016_j_apenergy_2023_121077 crossref_primary_10_1109_TCAD_2022_3207320 crossref_primary_10_1016_j_engappai_2025_110209 crossref_primary_10_1007_s00521_021_06169_2 crossref_primary_10_1088_1361_6501_aae5b2 crossref_primary_10_1155_2018_9327215 crossref_primary_10_1016_j_cie_2022_107970 crossref_primary_10_1016_j_neucom_2020_12_133 crossref_primary_10_1002_smll_202502328 crossref_primary_10_1007_s40314_025_03215_w crossref_primary_10_1007_s12519_025_00950_2 crossref_primary_10_1155_2021_6656150 crossref_primary_10_1016_j_swevo_2022_101120 crossref_primary_10_1007_s11042_021_11032_6 crossref_primary_10_1080_15472450_2022_2140046 crossref_primary_10_3233_JIFS_189039 crossref_primary_10_1111_jch_14597 |
| Cites_doi | 10.1016/j.patcog.2015.11.022 10.1093/bioinformatics/btw427 10.1162/neco.2006.18.7.1527 10.1007/978-3-319-48390-0_12 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Fei Ye. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 Fei Ye 2017 Fei Ye |
| Copyright_xml | – notice: COPYRIGHT 2017 Public Library of Science – notice: 2017 Fei Ye. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2017 Fei Ye 2017 Fei Ye |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY RC3 7X8 5PM DOA |
| DOI | 10.1371/journal.pone.0188746 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Materials Science Collection ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection ProQuest Biological Science Collection Agricultural Science Database Health & Medical Collection (Alumni) Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic Agricultural Science Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| DocumentTitleAlternate | A PSO-based deep learning approach |
| EISSN | 1932-6203 |
| ExternalDocumentID | 1976410096 oai_doaj_org_article_a17ec7a9020e40c0b6f16d1100a83108 PMC5728507 A518695765 29236718 10_1371_journal_pone_0188746 |
| Genre | Journal Article |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACCTH ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV ADRAZ AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAIFH BAWUL BBNVY BBTPI BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM ALIPV CGR CUY CVF ECM EIF IPNFZ NPM RIG BBORY 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO ESTFP FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI PRINS RC3 7X8 PUEGO 5PM - 02 AAPBV ABPTK ADACO BBAFP KM |
| ID | FETCH-LOGICAL-c622t-8a88701eae70bc9b18e339c1ee8a8927e16feceb25a713dcdd5b01ba56e1d6573 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 56 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000417884100028&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1932-6203 |
| IngestDate | Fri Nov 26 17:14:32 EST 2021 Tue Oct 14 19:00:16 EDT 2025 Tue Nov 04 01:59:17 EST 2025 Wed Oct 01 14:26:59 EDT 2025 Tue Oct 07 07:39:00 EDT 2025 Sat Nov 29 13:10:54 EST 2025 Sat Nov 29 09:59:33 EST 2025 Wed Nov 26 10:21:48 EST 2025 Wed Nov 26 10:22:22 EST 2025 Thu May 22 21:22:24 EDT 2025 Mon Jul 21 06:05:52 EDT 2025 Sat Nov 29 05:45:13 EST 2025 Tue Nov 18 22:26:20 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c622t-8a88701eae70bc9b18e339c1ee8a8927e16feceb25a713dcdd5b01ba56e1d6573 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. |
| ORCID | 0000-0002-5894-2178 |
| OpenAccessLink | https://doaj.org/article/a17ec7a9020e40c0b6f16d1100a83108 |
| PMID | 29236718 |
| PQID | 1976410096 |
| PQPubID | 1436336 |
| PageCount | e0188746 |
| ParticipantIDs | plos_journals_1976410096 doaj_primary_oai_doaj_org_article_a17ec7a9020e40c0b6f16d1100a83108 pubmedcentral_primary_oai_pubmedcentral_nih_gov_5728507 proquest_miscellaneous_1977218555 proquest_journals_1976410096 gale_infotracmisc_A518695765 gale_infotracacademiconefile_A518695765 gale_incontextgauss_ISR_A518695765 gale_incontextgauss_IOV_A518695765 gale_healthsolutions_A518695765 pubmed_primary_29236718 crossref_primary_10_1371_journal_pone_0188746 crossref_citationtrail_10_1371_journal_pone_0188746 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-12-13 |
| PublicationDateYYYYMMDD | 2017-12-13 |
| PublicationDate_xml | – month: 12 year: 2017 text: 2017-12-13 day: 13 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2017 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
| References | S. Zhang (ref45) 2014 A. Ratnaweera (ref61) 2004 H. Chen (ref2) 2016; 55 X. Ji (ref5) 2017; 122 ref59 D. Needell (ref48) 2016; 155 D. Romo-Bucheli (ref29) 2017; 91 J. Xu (ref23) 2016; 35 K. H. Cha (ref18) 2016; 43 B. Alipanahi (ref31) 2015; 33 I. Chaturvedi (ref9) 2016; 108 T. Yao (ref7) 2017; 64 S. Chen (ref25) 2017; 36 X. N. Fan (ref32) 2015; 11 Y. Chen (ref27) 2016; 32 J. Read (ref14) 2014; 85 ref42 K. Kamnitsas (ref24) 2017; 36 A. Nasef (ref46) 2017 H. Lodhi (ref36) 2012; 4 Q. Li (ref12) 2016; 107 S. Klein (ref47) 2009; 81 W. T. Pan (ref52) 2012; 26 G. Carneiro (ref15) 2012 N. Dhungel (ref21) 2017; 37 G Hinton (ref39) 2006; 18 V. K. Ithapu (ref44) 2015 S. Zhang (ref33) 2016; 44 ref4 Y. Lécun (ref63) 2001; 86 ref40 J. Schmidhuber (ref41) 2015; 61 S. Poria (ref10) 2016 ref30 T. Lei (ref13) 2015; 58 R. Baly (ref11) 2016; 35 Z. Yan (ref17) 2016; 35 ref38 Y. Tang (ref43) 2013 C. Angermueller (ref35) 2016; 12 Z. L. Gaing (ref54) 2004; 19 A. A. A. Esmin (ref56) 2005; 20 E. P. Ijjina (ref6) 2016 P. Wang (ref8) 2016; 46 M. Dorigo (ref53) 1997; 1 M. Anthimopoulos (ref22) 2016; 35 H. L. Chen (ref60) 2014; 239 G. Venter (ref50) 2013; 41 T. A. Ngo (ref20) 2017; 35 K. Ishaque (ref58) 2012; 27 ref28 D. R. Kelley (ref34) 2016; 26 F. Murtaza (ref3) 2017; 10 T. O. Ting (ref57) 2006; 21 V. Golkov (ref19) 2016; 35 M. R. Avendi (ref16) 2016; 30 N. Zhang (ref37) 2016 C. L. Huang (ref62) 2008; 8 W. A. Chang (ref49) 2002; 6 S. Das (ref51) 2011; 15 Y. Yi (ref1) 2016; 53 J. B. Park (ref55) 2005; 20 H. Greenspan (ref26) 2016; 35 |
| References_xml | – volume: 36 start-page: 802 issue: 3 year: 2017 ident: ref25 article-title: Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in ct images – volume: 21 start-page: 411 issue: 1 year: 2006 ident: ref57 article-title: A novel approach for unit commitment problem via an effective hybrid particle swarm optimization – volume: 53 start-page: 148 issue: C year: 2016 ident: ref1 article-title: Human action recognition with graph-based multiple-instance learning publication-title: Pattern Recognition doi: 10.1016/j.patcog.2015.11.022 – year: 2016 ident: ref6 article-title: Human action recognition using genetic algorithms and convolutional neural networks – volume: 239 start-page: 180 issue: 8 year: 2014 ident: ref60 article-title: Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy – volume: 11 start-page: 892 issue: 3 year: 2015 ident: ref32 article-title: Lncrna-mfdl: identification of human long non-coding rnas by fusing multiple features and using deep learning – ident: ref28 doi: 10.1093/bioinformatics/btw427 – start-page: 685 year: 2014 ident: ref45 article-title: Deep learning with elastic averaging sgd publication-title: Deep learning with elastic averaging sgd – volume: 35 start-page: 7 issue: 1 year: 2016 ident: ref11 article-title: A meta-framework for modeling the human reading process in sentiment analysis – volume: 20 start-page: 859 issue: 2 year: 2005 ident: ref56 article-title: A hybrid particle swarm optimization applied to loss power minimization – year: 2016 ident: ref37 article-title: Research on point-wise gated deep networks – volume: 35 start-page: 1207 issue: 5 year: 2016 ident: ref22 article-title: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network – volume: 86 start-page: 2278 issue: 11 year: 2001 ident: ref63 article-title: Gradient-based learning applied to document recognition – volume: 12 start-page: 878 issue: 7 year: 2016 ident: ref35 article-title: Deep learning for computational biology – ident: ref30 – volume: 155 start-page: 549 issue: 1–2 year: 2016 ident: ref48 article-title: Stochastic gradient descent, weighted sampling, and the randomized kaczmarz algorithm – volume: 1 start-page: 53 issue: 1 year: 1997 ident: ref53 article-title: Ant colony system: a cooperative learning approach to the traveling salesman problem – volume: 107 start-page: 289 issue: C year: 2016 ident: ref12 article-title: Mining opinion summarizations using convolutional neural networks in chinese microblogging systems – volume: 81 start-page: 227 issue: 3 year: 2009 ident: ref47 article-title: Adaptive stochastic gradient descent optimisation for image registration – volume: 26 start-page: 990 issue: 7 year: 2016 ident: ref34 article-title: Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks – volume: 35 start-page: 1332 issue: 5 year: 2016 ident: ref17 article-title: Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition – year: 2017 ident: ref46 article-title: Stochastic gradient descent analysis for the evaluation of a speaker recognition – volume: 26 start-page: 69 issue: 2 year: 2012 ident: ref52 article-title: A new fruit fly optimization algorithm: taking the financial distress model as an example – volume: 85 start-page: 333 issue: 3 year: 2014 ident: ref14 article-title: Deep learning for multi-label classification – ident: ref40 – volume: 58 start-page: 1151 issue: 3 year: 2015 ident: ref13 article-title: Molding cnns for text: non-linear, non-consecutive convolutions – volume: 19 start-page: 384 issue: 2 year: 2004 ident: ref54 article-title: A particle swarm optimization approach for optimum design of pid controller in avr system – volume: 55 start-page: 148 issue: C year: 2016 ident: ref2 article-title: A novel hierarchical framework for human action recognition – volume: 35 start-page: 1344 issue: 5 year: 2016 ident: ref19 article-title: Q-space deep learning: twelve-fold shorter and model-free diffusion mri scans – volume: 43 start-page: 1882 issue: 4 year: 2016 ident: ref18 article-title: Urinary bladder segmentation in ct urography using deep-learning convolutional neural network and level sets – volume: 18 start-page: 1527 issue: 7 year: 2006 ident: ref39 article-title: A Fast Learning Algorithm for Deep Belief Nets[J] publication-title: Neural Computation doi: 10.1162/neco.2006.18.7.1527 – year: 2013 ident: ref43 article-title: Deep learning using linear support vector machines – volume: 30 start-page: 108 year: 2016 ident: ref16 article-title: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri ☆ – volume: 35 start-page: 1153 issue: 5 year: 2016 ident: ref26 article-title: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique – volume: 33 start-page: 831 issue: 8 year: 2015 ident: ref31 article-title: Predicting the sequence specificities of dna- and rna-binding proteins by deep learning – volume: 44 start-page: e32 issue: 4 year: 2016 ident: ref33 article-title: A deep learning framework for modeling structural features of rna-binding protein targets – volume: 61 start-page: 85 year: 2015 ident: ref41 article-title: Deep learning in neural networks: an overview – year: 2012 ident: ref15 article-title: The Segmentation of the Left Ventricle of the Heart From Ultrasound Data Using Deep Learning Architectures and Derivative-Based Search Methods – volume: 41 start-page: 129 issue: 8 year: 2013 ident: ref50 article-title: Particle swarm optimization – volume: 108 start-page: 144 issue: C year: 2016 ident: ref9 article-title: Learning word dependencies in text by means of a deep recurrent belief network – volume: 36 start-page: 61 year: 2017 ident: ref24 article-title: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation – ident: ref38 doi: 10.1007/978-3-319-48390-0_12 – year: 2004 ident: ref61 article-title: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients – start-page: 488 year: 2015 ident: ref44 article-title: On the interplay of network structure and gradient convergence in deep learning – volume: 37 start-page: 114 year: 2017 ident: ref21 article-title: A deep learning approach for the analysis of masses in mammograms with minimal user intervention – volume: 6 start-page: 566 issue: 6 year: 2002 ident: ref49 article-title: A genetic algorithm for shortest path routing problem and the sizing of populations – volume: 27 start-page: 3627 issue: 8 year: 2012 ident: ref58 article-title: An improved particle swarm optimization (pso)–based mppt for pv with reduced steady-state oscillation – volume: 64 start-page: 236 issue: C year: 2017 ident: ref7 article-title: Learning universal multiview dictionary for human action recognition – volume: 10 start-page: 758 issue: 7 year: 2017 ident: ref3 article-title: Multi-view human action recognition using 2d motion templates based on mhis and their hog description – ident: ref4 – year: 2016 ident: ref10 article-title: Aspect extraction for opinion mining with a deep convolutional neural network – ident: ref59 – volume: 20 start-page: 34 issue: 1 year: 2005 ident: ref55 article-title: A particle swarm optimization for economic dispatch with nonsmooth cost functions – volume: 46 start-page: 498 issue: 4 year: 2016 ident: ref8 article-title: Action recognition from depth maps using deep convolutional neural networks – volume: 4 start-page: 455 issue: 5 year: 2012 ident: ref36 article-title: Computational biology perspective: kernel methods and deep learning – ident: ref42 – volume: 15 start-page: 4 issue: 1 year: 2011 ident: ref51 article-title: Differential evolution: a survey of the state-of-the-art – volume: 32 start-page: 1832 issue: 12 year: 2016 ident: ref27 article-title: Gene expression inference with deep learning – volume: 35 start-page: 159 year: 2017 ident: ref20 article-title: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance – volume: 35 start-page: 119 issue: 1 year: 2016 ident: ref23 article-title: Stacked sparse autoencoder (ssae) for nuclei detection on breast cancer histopathology images – volume: 91 start-page: 566 issue: 6 year: 2017 ident: ref29 article-title: A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers – volume: 8 start-page: 1381 issue: 4 year: 2008 ident: ref62 article-title: A distributed pso—svm hybrid system with feature selection and parameter optimization – volume: 122 start-page: 64 issue: C year: 2017 ident: ref5 article-title: The spatial laplacian and temporal energy pyramid representation for human action recognition using depth sequences |
| SSID | ssj0053866 |
| Score | 2.4722953 |
| Snippet | In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and... |
| SourceID | plos doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e0188746 |
| SubjectTerms | Algorithms Analysis Artificial intelligence Artificial neural networks Biological activity Biology and Life Sciences Classifiers Computer and Information Sciences Computer Simulation Configurations Construction Cooperative learning Engineering and Technology Mathematical models Mathematical programming Network configuration management software Neural networks Neural Networks, Computer Optimization Optimization theory Particle swarm optimization Pattern recognition systems Physical Sciences Power Research and Analysis Methods Searching Social Sciences Swarm intelligence |
| SummonAdditionalLinks | – databaseName: Nursing & Allied Health Database dbid: 7RV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwELag8MALY_zYCgMMQgIevMVOEydPaCAmkNCYBlR7ixzbGZXapNTt-E_4e7lz3NCgCZB4alVfkuZ8d_5sn78j5JmCsG_ikjP4giXMSsuUAr-qpEiNkEJFfuli_EEeH2dnZ_lJWHBzIa1yHRN9oDaNxjXyAw7j5ogj4n41_8awahTuroYSGlfJNY7YGOxZno7XkRh8OU3DcblY8oPQO_vzprb7EQf3Qti7MRx51v4uNg_m08ZdBjx_z5_cGJCOtv73VW6RmwGK0sPWdrbJFVvfJtvB2R19ERipX94hP06CgVH3XS1mtIE4MwsHOBmOg4aq1bLx7K8UycRnmGRDna-xAzIUkDE11s4p0mfCM-s2-dxRVRs6WcLnxj46ndR0ignqzIEBWS-DrMrMYCWClkWEYmbrXfLl6O3nN-9YKOjAdCrEkmUKdB5xq6yMSp2XPLNxnGtuLbTkQlqeVlbDXD9RMHc22pikjHipktRykyYyvkcGNXTeLqEir6wpAZvISI2EjjPAPVKaUTXKtTaJHJJ43a-FDmznWHRjWvgtPAmznlbLBVpDEaxhSFh31bxl-_iL_Gs0mU4Wubr9D83ivAg9UygurZYqB2BuR5GOyrTiqUGqPoVV3rIheYwGV7QHX7uIUxwmWC4M5oPJkDz1EsjXUWNC0LlaOVe8_zj-B6FPpz2h50GoakAdWoVDGPBOyAPWk9zrSULU0b3mXXSPtVZc8cuo4cq12V_e_KRrxptikl9tm5WXkYA4kwTuvtN6WKdZkSPVIAdlyZ7v9VTfb6knXz1deiJFBrOe-3_-Ww_IDYGIjQvG4z0yWC5W9iG5ri-WE7d45OPKTyEihqI priority: 102 providerName: ProQuest – databaseName: Public Library of Science (PLoS) Journals Open Access dbid: FPL link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELeg8MALMD62wgCDkICHjNhJ7ORxICqQplHxMe0tcmwHKrVJVbfwn_D3cue4YZk2AU-t6p_zcfad7-rz7wh5rsDsm6RiEXzBEmaVjZQCvaolF4ZLrmL_18XJkTw-zk9Pi-mfQPHcDn4i2esg04Nl29iDmIFSpOIqucYTITDYmkyPtpYXdFeIcDzusp6D5cez9Pe2eLSct-4iR_N8vuSZBWhy638f_Ta5GVxNetjNjR1yxTZ3yE5QZkdfBsbpV3fJr2mYQNT9VKsFbcGOLMIBzQjXOUPVZt16dleKZOELTKKhztfQAQwFz5caa5cU6THhnk2XXO6oagydreHzzD45nTV0jgnokYMJYj0GWZMjg5UGOpYQipmr98jXybsvb99HoWBDpAXn6yhX8Ioxs8rKuNJFxXKbJIVm1kJLwaVlorYaYvlMQWxstDFZFbNKZcIyIzKZ3CejBmS1Rygvamsq8D1krFKukxz8GilNWqeF1iaTY5Jsx7HUgc0ci2rMS79FJyGq6aRcovDLIPwxifpey47N4y_4NzhFeixycfsfYJTLMDKlYtJqqQpwvG0a67gSNRMGqfgUVnHLx-QJTrCyO9jaW5TyMMNyYBDvZWPyzCOQj6PBhJ9vauNc-eHjyT-APn8agF4EUN2COLQKhyzgnZDna4DcHyDBquhB8x6qw1YqrmTgt6YMI17ouVWRi5uf9s14UUzia2y78RgJHmWWwdV3O43qJcsLpBJkICw50LWB6Ictzey7p0PPJM8hqnlw-RM_JDc4emOMRyzZJ6P1amMfkev6x3rmVo-9DfkNzwF04w priority: 102 providerName: Public Library of Science |
| Title | Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29236718 https://www.proquest.com/docview/1976410096 https://www.proquest.com/docview/1977218555 https://pubmed.ncbi.nlm.nih.gov/PMC5728507 https://doaj.org/article/a17ec7a9020e40c0b6f16d1100a83108 http://dx.doi.org/10.1371/journal.pone.0188746 |
| Volume | 12 |
| WOSCitedRecordID | wos000417884100028&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: P5Z dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M0K dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7P dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7S dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PATMY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7X7 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KB. dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7RV dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: BENPR dateStart: 20061201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PIMPY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8C1 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVATS databaseName: Public Library of Science (PLoS) Journals Open Access (WRLC) customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: FPL dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.plos.org/publications/ providerName: Public Library of Science |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1fb9MwELeg8MALYvxbYRSDkICHdHHSxMnjOq1i2laiDqqxl8ixHajUJlXTwjfh83LnuFGDJo0HXq5VfU6bO9_5rjn_jpB3Aty-8jPmwBtsYZZpRwiwq5x7ofK4J1zz18X0nI_H0dVVnOy0-sKasBoeuBbcoWBcSy5iCGv0wJVuFuYsVAh0JrBHljnm6_J4m0zVPhisOAztQTmfs0Orl_6yLHTfZWBYGPDubEQGr7_xyp3lvKxuCjn_rpzc2YpGj8hDG0PSo_q375E7unhM9qyVVvSDhZL--IT8TuwN0uqXWC1oCQ5iYU9eOriBKSo269LAtlJEAV9gdQytTHMc4KEQ0lKl9ZIi7iV8Z1FXjVdUFIrO1vC68wCczgo6x8pypwLNa8ODcMiOwhYCNfwHxZLUp-Tr6OTL8SfHdmJwZOh5aycSIDKXaaG5m8k4Y5H2_VgyrWEk9rhmYa4lJOmBgKRXSaWCzGWZCELNVBhw_xnpFCD7fUK9ONcqg6CCu2LgST-CgIVzNcgHsZQq4F3ib9WSSgtTjt0y5ql59sYhXamlnKIyU6vMLnGaWcsapuMW_iFqvOFFkG3zASy91GomvW3pdclrXC9pfWK1cRXpUYB9viCRC7rkreFAoI0CK3m-i01Vpaefp__AdDlpMb23THkJ4pDCnp6Ae0IArxbnQYsT3IVsDe_j6t5KpUoZBKQDhqkszNyu-JuH3zTDeFGszit0uTE8HELFIICrP68NpJGsFyNGIANh8ZbptETfHilmPwzOecC9CNKVF_9DVy_JAw8DMuY5zD8gnfVqo1-R-_LnelateuQun0yRXnFDI6DRMeuRe8OTcTLpGecCdJScAz0b9oFeuGdIeWLoJdAkuIYZyelF8u0PdreEHw |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VgAQXoLwaKHRBIODg1rt-bHxAqDyqVg0hglJVXMx6d10iJXaIEyr-CD-D38iMvTYxqoBLD5wSZcdrZzzz7Yw9-w0hDyXAvvYS5sAXbGGWGEdK8KtU8FBzwaVbPro47IvBoHd0FA1XyI96LwyWVdaYWAK1zhU-I99isG76DCPu59MvDnaNwrerdQuNyiz2zbcTSNmKZ3uv4P4-4nzn9cHLXcd2FXBUyPnc6UnwK5cZaYSbqChhPeN5kWLGwEjEhWFhahQknIGEBE4rrYPEZYkMQsN0GAgP5j1Hzvs-d9GLhsHHGvkBO8LQbs_zBNuy1rA5zTOz6TI4LYbZS8tf2SWgWQs603FenBbo_l6vubQA7lz531R3lVy2oTbdrnxjlayY7BpZtWBW0CeWcfvpdfJ9aB2IFidyNqE54OjEblB1cJ3XVC7mecluS5EsfYJFRLQoewiBDIXIn2pjphTpQeGcWVVcX1CZaTqaw-dSnQAdZXSMBfhOAQ5iShlkjXY0dlqoWFIoVu7eIB_ORD83SScDY1kjlEep0QnEXsKVPldeD-I6IbSf-pFSOhBd4tV2FCvL5o5NRcZx-YpSQFZXaTlG64ut9XWJ0xw1rdhM_iL_Ak20kUUu8vKHfHYc2zsTSyaMEjKCxMP4rnKTMGWhRipCiV3sel2ygQYeVxt7G0SNtwNshwb5btAlD0oJ5CPJsODpWC6KIt57e_gPQu_ftYQeW6E0B3UoaTeZwH9CnrOW5HpLElBVtYbX0B1rrRTxLyeCI2s3O334fjOMk2IRY2byRSkjIKIOApj9VuXRjWZ5hFSKDJQlWr7eUn17JBt9LungA8F7kNXd_vNlbZCLuwdv-nF_b7B_h1ziGJ0y7jBvnXTms4W5Sy6or_NRMbtXYholn84aCX4CkH_ldQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VgBAXoLwaKHRBIODgxruOvfYBoUKJqFqFiEdVcTHr3XWJlNghTqj4I_wYfh0z9trEqAIuPXBKlBmvnfE87dlvCHkowe1rL2EOfMERZolxpAS7SgUPNBdcuuWji8MDMRyGR0fRaI38qPfCYFtl7RNLR61zhc_IewziZp9hxt1LbVvEaHfwfPbFwQlS-Ka1HqdRqci--XYC5VvxbG8X7vUjzgev3r987dgJA44KOF84oQQbc5mRRriJihIWGs-LFDMGKBEXhgWpUVB8-hKKOa209hOXJdIPDNOBLzxY9xw5L6DGxHbCkf-xjgLgR4LAbtXzBOtZzdie5ZnZdhmcFlPulVBYTgxo4kJnNsmL05Le33s3V4Lh4Mr_LMar5LJNwelOZTPrZM1k18i6dXIFfWKRuJ9eJ99H1rBocSLnU5qDf53ajasOxn9N5XKRl6i3FEHUp9hcRItythDwUKgIqDZmRhE2FM6ZVU33BZWZpuMFfK70D9BxRifYmO8UYDim5EE0aUfjBIYKPYViR-8N8uFM5HOTdDJQnA1CeZQanUBOJlzZ58oLId8TQvfTfqSU9kWXeLVOxcqivOOwkUlcvroUUO1VUo5RE2OriV3iNEfNKpSTv_C_QHVteBGjvPwhnx_H9s7EkgmjhIygIDF9V7lJkLJAI0ShxOl2YZdsobLH1YbfxtPGOz6OSYM62O-SByUH4pRkqKrHclkU8d6bw39geve2xfTYMqU5iENJu_kE_hPin7U4N1uc4G1Vi7yBpllLpYh_GRQcWZvc6eT7DRkXxebGzOTLkkdApu37sPqtyrobyfIIIRYZCEu07L4l-jYlG38uYeJ9wUOo9m7_-bK2yEVwAPHB3nD_DrnEMWll3GHeJuks5ktzl1xQXxfjYn6vdG-UfDprR_ATGILuPw |
| 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=Particle+swarm+optimization-based+automatic+parameter+selection+for+deep+neural+networks+and+its+applications+in+large-scale+and+high-dimensional+data&rft.jtitle=PloS+one&rft.au=Ye%2C+Fei&rft.date=2017-12-13&rft.eissn=1932-6203&rft.volume=12&rft.issue=12&rft.spage=e0188746&rft_id=info:doi/10.1371%2Fjournal.pone.0188746&rft_id=info%3Apmid%2F29236718&rft.externalDocID=29236718 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon |