SAKM: Self-adaptive kernel machine A kernel-based algorithm for online clustering
This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account varia...
Gespeichert in:
| Veröffentlicht in: | Neural networks Jg. 21; H. 9; S. 1287 - 1301 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Kidlington
Elsevier Ltd
01.11.2008
Elsevier |
| Schlagworte: | |
| ISSN: | 0893-6080, 1879-2782 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets.
After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment. |
|---|---|
| AbstractList | This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment.This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment. This paper presents a new online clustering algorithm called SAKM (Self- Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment. This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters from non-stationary data. Based on SVM and kernel methods, the SAKM algorithm uses a fast adaptive learning procedure to take into account variations over time. Dedicated to online clustering in a multi-class environment, the algorithm designs an unsupervised neural architecture with self-adaptive abilities. Based on a specific kernel-induced similarity measure, the SAKM learning procedures consist of four main stages: Creation, Adaptation, Fusion and Elimination. In addition to these properties, the SAKM algorithm is attractive to be computationally efficient in online learning of real-drifting targets. After a theoretical study of the error convergence bound of the SAKM local learning, a comparison with NORMA and ALMA algorithms is made. In the end, some experiments conducted on simulation data, UCI benchmarks and real data are given to illustrate the capacities of the SAKM algorithm for online clustering in non-stationary and multi-class environment. |
| Author | Maouche, Salah Amadou Boubacar, Habiboulaye Lecoeuche, Stéphane |
| Author_xml | – sequence: 1 givenname: Habiboulaye surname: Amadou Boubacar fullname: Amadou Boubacar, Habiboulaye email: amadou@ensm-douai.fr organization: Ecole des Mines de Douai, Département Informatique et Automatique, 941, Rue Charles Bourseul, BP838, 59 508 Douai, France – sequence: 2 givenname: Stéphane surname: Lecoeuche fullname: Lecoeuche, Stéphane email: lecoeuche@ensm-douai.fr organization: Ecole des Mines de Douai, Département Informatique et Automatique, 941, Rue Charles Bourseul, BP838, 59 508 Douai, France – sequence: 3 givenname: Salah surname: Maouche fullname: Maouche, Salah email: salah.maouche@univ-lille.fr organization: Laboratoire Automatique, Génie Informatique et Signal, UMR CNRS 8146, Université des Sciences et Technologies de Lille, Bâtiment P2, 59655 Villeneuve d’Ascq, France |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20887283$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/18835695$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkU9r3DAUxEVJaTZpv0EpvjQ3u0-WbUk5FJbQfyQhlOQuZPk50VaWtpIc6Levl93m0ENyejD8ZhjenJAjHzwS8p5CRYF2nzaVx9ljrmoAUQGrFvEVWVHBZVlzUR-RFQjJyg4EHJOTlDYA0ImGvSHHVAjWdrJdkZ-368vr8-IW3VjqQW-zfcTiF0aPrpi0ebAei_VBKHudcCi0uw_R5oepGEMsgnc7xrg5ZYzW378lr0ftEr473FNy9_XL3cX38urm24-L9VVpmprmUnIjpUDO27rVYmxx6BkwwVgrB5S66TQMo-S0E9T0yJueGs01oOkpxZ6xU3K2j93G8HvGlNVkk0HntMcwJ9VJ3tVd27wIUtlAyyks4IcDOPcTDmob7aTjH_XvWQvw8QDoZLQbo_bGpieuBiF4LXbVzveciSGliKMyNutsg89RW6coqN2CaqP2C6rdggqYWsTF3PxnfurxvO3z3obLyx8tRpWMRW9wsBFNVkOwzwf8BbeStmk |
| CitedBy_id | crossref_primary_10_3390_math12243935 crossref_primary_10_1016_j_ress_2014_12_005 crossref_primary_10_1016_j_neucom_2014_02_036 crossref_primary_10_1016_j_ins_2010_02_005 crossref_primary_10_1007_s10044_017_0608_9 crossref_primary_10_1517_17425250902753261 |
| Cites_doi | 10.1162/089976602317250933 10.1109/TSP.2004.830991 10.1109/3477.969494 10.1145/380995.380999 10.1109/72.712183 10.1162/089976600300015565 10.1007/s00521-004-0427-y 10.1109/TFUZZ.1993.390282 10.1016/S0925-2312(02)00599-4 10.1162/089976601750264965 10.1109/72.788640 10.1023/A:1009715923555 10.1109/TSP.2005.851098 10.1109/IJCNN.2001.938743 10.1016/S0031-3203(03)00140-7 |
| ContentType | Journal Article |
| Copyright | 2008 Elsevier Ltd 2009 INIST-CNRS |
| Copyright_xml | – notice: 2008 Elsevier Ltd – notice: 2009 INIST-CNRS |
| DBID | AAYXX CITATION IQODW CGR CUY CVF ECM EIF NPM 7TK 7X8 |
| DOI | 10.1016/j.neunet.2008.03.016 |
| DatabaseName | CrossRef Pascal-Francis Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Neurosciences Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Neurosciences Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Neurosciences Abstracts MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science Applied Sciences |
| EISSN | 1879-2782 |
| EndPage | 1301 |
| ExternalDocumentID | 18835695 20887283 10_1016_j_neunet_2008_03_016 S0893608008001184 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M -~X .DC .~1 0R~ 123 186 1B1 1RT 1~. 1~5 29N 4.4 457 4G. 53G 5RE 5VS 6TJ 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXLA AAXUO AAYFN ABAOU ABBOA ABCQJ ABEFU ABFNM ABFRF ABHFT ABIVO ABJNI ABLJU ABMAC ABXDB ABYKQ ACAZW ACDAQ ACGFO ACGFS ACIUM ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADRHT AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q G8K GBLVA GBOLZ HLZ HMQ HVGLF HZ~ IHE J1W JJJVA K-O KOM KZ1 LG9 LMP M2V M41 MHUIS MO0 MOBAO MVM N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SCC SDF SDG SDP SES SEW SNS SPC SPCBC SSN SST SSV SSW SSZ T5K TAE UAP UNMZH VOH WUQ XPP ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD AGCQF AGRNS BNPGV IQODW SSH CGR CUY CVF ECM EIF NPM 7TK 7X8 |
| ID | FETCH-LOGICAL-c421t-97c998e77525a8f5edb30383359de9a46a0df971681cbe74b1ca7a0ecb11eb33 |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000261550100010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0893-6080 |
| IngestDate | Sun Sep 28 06:15:38 EDT 2025 Sat Sep 27 21:10:59 EDT 2025 Mon Jul 21 06:02:22 EDT 2025 Mon Jul 21 09:15:30 EDT 2025 Tue Nov 18 22:35:02 EST 2025 Sat Nov 29 02:39:39 EST 2025 Fri Feb 23 02:29:04 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Keywords | Online clustering RKHS Non-stationary data Multi-class problems Evolving models Cluster analysis Automatic classification On line Similarity Adaptability Support vector machine Approximation algorithm Combinatorial optimization Adaptive method Hilbert space Architecture Theoretical study Error bound Non stationary condition On-line systems Experimental study Signal classification Unsupervised learning Kernel method Simulation Metric Comparative study |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 CC BY 4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c421t-97c998e77525a8f5edb30383359de9a46a0df971681cbe74b1ca7a0ecb11eb33 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PMID | 18835695 |
| PQID | 19405710 |
| PQPubID | 23462 |
| PageCount | 15 |
| ParticipantIDs | proquest_miscellaneous_69762654 proquest_miscellaneous_19405710 pubmed_primary_18835695 pascalfrancis_primary_20887283 crossref_citationtrail_10_1016_j_neunet_2008_03_016 crossref_primary_10_1016_j_neunet_2008_03_016 elsevier_sciencedirect_doi_10_1016_j_neunet_2008_03_016 |
| PublicationCentury | 2000 |
| PublicationDate | 2008-11-01 |
| PublicationDateYYYYMMDD | 2008-11-01 |
| PublicationDate_xml | – month: 11 year: 2008 text: 2008-11-01 day: 01 |
| PublicationDecade | 2000 |
| PublicationPlace | Kidlington |
| PublicationPlace_xml | – name: Kidlington – name: United States |
| PublicationTitle | Neural networks |
| PublicationTitleAlternate | Neural Netw |
| PublicationYear | 2008 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Gentile (b17) 2001; 2 Kriegel, Sander, Ester, Xu (b21) 1997; 2 Ben-Hur, Horn, Siegelmann, Vapnik (b3) 2001; 2 Bennett, Campbell (b4) 2000; 2 Laboratoire LAGIS. Université de Sciences et Technologies de Lille 1 (USTL) Blake, C., & Merz, C. (1998). UCI Repository of machine learning databases. Schölkopf, Platt, Shawe-Taylor, Smola (b25) 2001; 13 Bishop (b5) 1995 Cheong, Oh, Lee (b12) 2004; 2 Kivinen, Smola, Williamson (b20) 2004; 52 TU Deft, Netherlands Vapnik (b31) 1998 Amadou, B. H., Lecoeuche, S., & Maouche, S. (2005). Self-adaptive kernel machine: Online clustering in RKHS. In Borer, S. (2003). New support vector algorithms for multi-categorical data: Applied to real-time object recognition. Tax, D. (2001). One-class classification. Schölkopf, Smola (b27) 2002 Burges (b10) 1998; 2 (pp. 2404–2409). Simpson (b28) 1993; 11 Lausanne Swiss EPFL Bordes, Bottou (b7) 2005; Vol. 3720 Gretton, Desobry (b18) 2003; vol. 2 Amadou-Boubacar, H. (2006). Classification Dynamique de données non-stationnaires: Apprentissage et Suivi de classes évolutives. Kasabov (b19) 2001; 31 Bordes, Ertekin, Weston, Bottou (b8) 2005; 6 Cauwenberghs, Poggio (b11) 2000; Vol. 13 Tax, Laskov (b29) 2003 Lecoeuche, Lurette (b24) 2003 Csato, Opper (b13) 2002; 14 Eltoft, Figueiredo (b16) 1998; 9 Deng, Kasabov (b14) 2003; 51 Desobry, Davy, Doncarli (b15) 2005; 53 Lecoeuche, Lurette (b23) 2004; 13 Kuh, A. (2001). Adaptive kernel methods for CDMA systems. In Vapnik (b32) 1999; 10 Zhang, Xing (b33) 2003; 37 Schölkopf, Smola, Williamson, Bartlett (b26) 2000; 12 Burges (10.1016/j.neunet.2008.03.016_b10) 1998; 2 Gretton (10.1016/j.neunet.2008.03.016_b18) 2003; vol. 2 Lecoeuche (10.1016/j.neunet.2008.03.016_b24) 2003 Schölkopf (10.1016/j.neunet.2008.03.016_b26) 2000; 12 Bishop (10.1016/j.neunet.2008.03.016_b5) 1995 10.1016/j.neunet.2008.03.016_b22 Vapnik (10.1016/j.neunet.2008.03.016_b32) 1999; 10 Simpson (10.1016/j.neunet.2008.03.016_b28) 1993; 11 Lecoeuche (10.1016/j.neunet.2008.03.016_b23) 2004; 13 Desobry (10.1016/j.neunet.2008.03.016_b15) 2005; 53 Eltoft (10.1016/j.neunet.2008.03.016_b16) 1998; 9 Bordes (10.1016/j.neunet.2008.03.016_b7) 2005; Vol. 3720 Cauwenberghs (10.1016/j.neunet.2008.03.016_b11) 2000; Vol. 13 Tax (10.1016/j.neunet.2008.03.016_b29) 2003 Cheong (10.1016/j.neunet.2008.03.016_b12) 2004; 2 Bennett (10.1016/j.neunet.2008.03.016_b4) 2000; 2 Zhang (10.1016/j.neunet.2008.03.016_b33) 2003; 37 Gentile (10.1016/j.neunet.2008.03.016_b17) 2001; 2 Csato (10.1016/j.neunet.2008.03.016_b13) 2002; 14 10.1016/j.neunet.2008.03.016_b30 Kriegel (10.1016/j.neunet.2008.03.016_b21) 1997; 2 Schölkopf (10.1016/j.neunet.2008.03.016_b25) 2001; 13 Kivinen (10.1016/j.neunet.2008.03.016_b20) 2004; 52 10.1016/j.neunet.2008.03.016_b6 Ben-Hur (10.1016/j.neunet.2008.03.016_b3) 2001; 2 10.1016/j.neunet.2008.03.016_b9 10.1016/j.neunet.2008.03.016_b2 Deng (10.1016/j.neunet.2008.03.016_b14) 2003; 51 Schölkopf (10.1016/j.neunet.2008.03.016_b27) 2002 Vapnik (10.1016/j.neunet.2008.03.016_b31) 1998 10.1016/j.neunet.2008.03.016_b1 Kasabov (10.1016/j.neunet.2008.03.016_b19) 2001; 31 Bordes (10.1016/j.neunet.2008.03.016_b8) 2005; 6 |
| References_xml | – reference: . TU Deft, Netherlands – volume: 37 start-page: 131 year: 2003 end-page: 144 ident: b33 article-title: Competitive EM algorithm for finite mixture models publication-title: Pattern Recognition – volume: 2 start-page: 47 year: 2004 end-page: 51 ident: b12 article-title: Support vector machines with binary tree architecture for multi-class classification publication-title: Neural Information Processing – volume: 31 start-page: 902 year: 2001 end-page: 918 ident: b19 article-title: Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning publication-title: IEEE Transactions of Systems, Man and Cybernetics, Part B–Cybernetics – volume: 2 start-page: 169 year: 1997 end-page: 194 ident: b21 article-title: Density-based clustering in spatial databases: The algorithm gdbscan and its applications publication-title: Data Mining and Knowledge Discovery – reference: Amadou-Boubacar, H. (2006). Classification Dynamique de données non-stationnaires: Apprentissage et Suivi de classes évolutives. – volume: 52 start-page: 2165 year: 2004 end-page: 2176 ident: b20 article-title: Online Learning with Kernels publication-title: IEEE Transactions on Signal Processing – volume: 12 start-page: 1207 year: 2000 end-page: 1245 ident: b26 article-title: New support vector algorithms publication-title: Neural Computation – volume: Vol. 13 start-page: 409 year: 2000 end-page: 415 ident: b11 publication-title: Incremental and decremental support vector machine learning – reference: . Laboratoire LAGIS. Université de Sciences et Technologies de Lille 1 (USTL) – volume: 14 start-page: 641 year: 2002 end-page: 668 ident: b13 article-title: Sparse online gaussian processes publication-title: Neural Computation – volume: 2 year: 2001 ident: b17 article-title: A new approximation maximal margin classification algorithm publication-title: Journal of Machine Learning Research – year: 1998 ident: b31 article-title: Statistical learning theory – volume: vol. 2 start-page: 709 year: 2003 end-page: 712 ident: b18 article-title: Online one-class nu-svm, an application to signal segmentation publication-title: IEEE ICASSP03 proceedings – volume: 2 start-page: 1 year: 2000 end-page: 13 ident: b4 article-title: Support vector machines: Hype or hallelujah? publication-title: SIGKDD Exploration – reference: Borer, S. (2003). New support vector algorithms for multi-categorical data: Applied to real-time object recognition. – reference: Kuh, A. (2001). Adaptive kernel methods for CDMA systems. In – volume: 13 start-page: 1443 year: 2001 end-page: 1471 ident: b25 article-title: Estimating the support of a high-dimensional distribution publication-title: Neural Computation – volume: 2 start-page: 125 year: 2001 end-page: 137 ident: b3 article-title: Support vector clustering publication-title: Journal of Machine Learning Research – volume: 13 start-page: 323 year: 2004 end-page: 338 ident: b23 article-title: New supervision architecture based on on line modelization of non stationary data publication-title: Neural Computing and Applications – reference: (pp. 2404–2409). – volume: 51 start-page: 87 year: 2003 end-page: 103 ident: b14 article-title: On-line pattern analysis by evolving self organizing maps publication-title: Neurocomputing – volume: 9 start-page: 1021 year: 1998 end-page: 1035 ident: b16 article-title: A new neural network for cluster-detection-and-labeling publication-title: IEEE Transactions on Neural Networks – reference: Amadou, B. H., Lecoeuche, S., & Maouche, S. (2005). Self-adaptive kernel machine: Online clustering in RKHS. In – volume: 2 start-page: 121 year: 1998 end-page: 167 ident: b10 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Mining and Knowledge Discovery – volume: Vol. 3720 start-page: 505 year: 2005 end-page: 512 ident: b7 article-title: The Huller: A simple and efficient online SVM publication-title: Machine learning: ECML 2005 – reference: Blake, C., & Merz, C. (1998). UCI Repository of machine learning databases. – volume: 53 start-page: 2961 year: 2005 end-page: 2974 ident: b15 article-title: An online Kernel change detection algorithm publication-title: IEEE Transactions on Signal Processing – start-page: 499 year: 2003 end-page: 508 ident: b29 article-title: Online SVM learning: From classification to data description and back publication-title: Proceedings of the Neural Network and Signal Processing – volume: 11 start-page: 32 year: 1993 end-page: 45 ident: b28 article-title: Fuzzy min–max neural networks — Part2: Classification publication-title: IEEE Transactions on Fuzzy systems – volume: 6 start-page: 1579 year: 2005 end-page: 1619 ident: b8 article-title: Fast Kernel classifiers with online and active learning publication-title: Journal of Machine Learning Research – start-page: 350 year: 2003 end-page: 358 ident: b24 article-title: Auto-adaptive and dynamical clustering neural network publication-title: ICANN03 – year: 1995 ident: b5 article-title: Neural networks for pattern recognition – reference: Tax, D. (2001). One-class classification. – volume: 10 start-page: 988 year: 1999 end-page: 999 ident: b32 article-title: An overview of statistical learning theory publication-title: IEEE Transactions on Neural Networks – reference: . Lausanne Swiss EPFL – year: 2002 ident: b27 article-title: Learning with Kernels – ident: 10.1016/j.neunet.2008.03.016_b2 – volume: 2 year: 2001 ident: 10.1016/j.neunet.2008.03.016_b17 article-title: A new approximation maximal margin classification algorithm publication-title: Journal of Machine Learning Research – volume: 2 start-page: 47 issue: 3 year: 2004 ident: 10.1016/j.neunet.2008.03.016_b12 article-title: Support vector machines with binary tree architecture for multi-class classification publication-title: Neural Information Processing – volume: 14 start-page: 641 year: 2002 ident: 10.1016/j.neunet.2008.03.016_b13 article-title: Sparse online gaussian processes publication-title: Neural Computation doi: 10.1162/089976602317250933 – volume: 52 start-page: 2165 issue: 8 year: 2004 ident: 10.1016/j.neunet.2008.03.016_b20 article-title: Online Learning with Kernels publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2004.830991 – year: 1995 ident: 10.1016/j.neunet.2008.03.016_b5 – volume: 2 start-page: 125 year: 2001 ident: 10.1016/j.neunet.2008.03.016_b3 article-title: Support vector clustering publication-title: Journal of Machine Learning Research – volume: 31 start-page: 902 issue: 6 year: 2001 ident: 10.1016/j.neunet.2008.03.016_b19 article-title: Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning publication-title: IEEE Transactions of Systems, Man and Cybernetics, Part B–Cybernetics doi: 10.1109/3477.969494 – ident: 10.1016/j.neunet.2008.03.016_b6 – volume: 2 start-page: 1 issue: 2 year: 2000 ident: 10.1016/j.neunet.2008.03.016_b4 article-title: Support vector machines: Hype or hallelujah? publication-title: SIGKDD Exploration doi: 10.1145/380995.380999 – volume: Vol. 3720 start-page: 505 year: 2005 ident: 10.1016/j.neunet.2008.03.016_b7 article-title: The Huller: A simple and efficient online SVM – volume: 9 start-page: 1021 issue: 5 year: 1998 ident: 10.1016/j.neunet.2008.03.016_b16 article-title: A new neural network for cluster-detection-and-labeling publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.712183 – volume: 12 start-page: 1207 year: 2000 ident: 10.1016/j.neunet.2008.03.016_b26 article-title: New support vector algorithms publication-title: Neural Computation doi: 10.1162/089976600300015565 – volume: 13 start-page: 323 issue: 4 year: 2004 ident: 10.1016/j.neunet.2008.03.016_b23 article-title: New supervision architecture based on on line modelization of non stationary data publication-title: Neural Computing and Applications doi: 10.1007/s00521-004-0427-y – start-page: 499 year: 2003 ident: 10.1016/j.neunet.2008.03.016_b29 article-title: Online SVM learning: From classification to data description and back publication-title: Proceedings of the Neural Network and Signal Processing – volume: vol. 2 start-page: 709 year: 2003 ident: 10.1016/j.neunet.2008.03.016_b18 article-title: Online one-class nu-svm, an application to signal segmentation – ident: 10.1016/j.neunet.2008.03.016_b30 – volume: 11 start-page: 32 year: 1993 ident: 10.1016/j.neunet.2008.03.016_b28 article-title: Fuzzy min–max neural networks — Part2: Classification publication-title: IEEE Transactions on Fuzzy systems doi: 10.1109/TFUZZ.1993.390282 – ident: 10.1016/j.neunet.2008.03.016_b1 – volume: Vol. 13 start-page: 409 year: 2000 ident: 10.1016/j.neunet.2008.03.016_b11 – start-page: 350 year: 2003 ident: 10.1016/j.neunet.2008.03.016_b24 article-title: Auto-adaptive and dynamical clustering neural network – volume: 51 start-page: 87 year: 2003 ident: 10.1016/j.neunet.2008.03.016_b14 article-title: On-line pattern analysis by evolving self organizing maps publication-title: Neurocomputing doi: 10.1016/S0925-2312(02)00599-4 – volume: 13 start-page: 1443 year: 2001 ident: 10.1016/j.neunet.2008.03.016_b25 article-title: Estimating the support of a high-dimensional distribution publication-title: Neural Computation doi: 10.1162/089976601750264965 – volume: 10 start-page: 988 issue: 5 year: 1999 ident: 10.1016/j.neunet.2008.03.016_b32 article-title: An overview of statistical learning theory publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.788640 – ident: 10.1016/j.neunet.2008.03.016_b9 – volume: 2 start-page: 121 year: 1998 ident: 10.1016/j.neunet.2008.03.016_b10 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Mining and Knowledge Discovery doi: 10.1023/A:1009715923555 – volume: 53 start-page: 2961 issue: 8 year: 2005 ident: 10.1016/j.neunet.2008.03.016_b15 article-title: An online Kernel change detection algorithm publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2005.851098 – volume: 6 start-page: 1579 year: 2005 ident: 10.1016/j.neunet.2008.03.016_b8 article-title: Fast Kernel classifiers with online and active learning publication-title: Journal of Machine Learning Research – year: 1998 ident: 10.1016/j.neunet.2008.03.016_b31 – ident: 10.1016/j.neunet.2008.03.016_b22 doi: 10.1109/IJCNN.2001.938743 – year: 2002 ident: 10.1016/j.neunet.2008.03.016_b27 – volume: 37 start-page: 131 year: 2003 ident: 10.1016/j.neunet.2008.03.016_b33 article-title: Competitive EM algorithm for finite mixture models publication-title: Pattern Recognition doi: 10.1016/S0031-3203(03)00140-7 – volume: 2 start-page: 169 issue: 2 year: 1997 ident: 10.1016/j.neunet.2008.03.016_b21 article-title: Density-based clustering in spatial databases: The algorithm gdbscan and its applications publication-title: Data Mining and Knowledge Discovery |
| SSID | ssj0006843 |
| Score | 1.9682572 |
| Snippet | This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine) which is developed to learn continuously evolving clusters... This paper presents a new online clustering algorithm called SAKM (Self- Adaptive Kernel Machine) which is developed to learn continuously evolving clusters... |
| SourceID | proquest pubmed pascalfrancis crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1287 |
| SubjectTerms | Algorithms Applied sciences Artificial Intelligence Cluster Analysis Evolving models Exact sciences and technology Flows in networks. Combinatorial problems Information, signal and communications theory Models, Statistical Multi-class problems Non-stationary data Online clustering Online Systems Operational research and scientific management Operational research. Management science Risk RKHS Signal and communications theory Signal representation. Spectral analysis Signal, noise Stochastic Processes Telecommunications and information theory |
| Title | SAKM: Self-adaptive kernel machine A kernel-based algorithm for online clustering |
| URI | https://dx.doi.org/10.1016/j.neunet.2008.03.016 https://www.ncbi.nlm.nih.gov/pubmed/18835695 https://www.proquest.com/docview/19405710 https://www.proquest.com/docview/69762654 |
| Volume | 21 |
| WOSCitedRecordID | wos000261550100010&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: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-2782 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006843 issn: 0893-6080 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Jj9owFLYYpodKVfeFLtSH3pBHMVkc98ZQKrrMUHVA4hY5idOBZgJiGU3v_eF9ju3AtINoD71E6OFgmff5LfZbEHqTCg5mKqUkBeFPvDAISJxyRjKHJ5kI_NThutkEOz0Nx2P-pVb7aXNhLnNWFOHVFZ__V1YDDZitUmf_gd3VjwIBPgPT4Qlsh-dfMf6s8-mkdPTPZJ4RkYp5GR30XS4KmbcuyuBJCfJAE4hSY2lL5N9mi8nq_KIMO9TlM1pJvlZlFKxym9pCT2WljkLHj1cmOQjvd4NR63gwOgb2luEVfRFP4tk6Fz8q9HzudQe9Ubff0xFm-pp-fi42l_snnUH1vcjNabU9lghNfl51VmYU-7Y44y4JHN23ycpenR1tMMa3BCmoTballEHT0hsFvj57mB4Vcg3rNrGx7pFDb6iv_Zveq6IR20rSgpl1gA7bzOdhHR12PvTGHyuNHoQ6-tKuwKZglnGCf868y8S5MxdL2HiZ7piy26UpTZvhfXTX-CS4o7H0ANVk8RDdM_4JNtJ_CSTbAsTSHqGvCm34Lb6GNayhhQ3WcAdvYw1XWMOANayxhjdYe4yG73vDbp-YNh0k8dp0RThLwGeXjPltX4SZL9MY7CKVzMdTyYUXCCfNVKWykCaxZF5ME8GEI5OYUhm77hNUL2aFfIZw6koBvFC1sxNPiABGMxicMSlZzGLaQK79Y6PElLBXnVTyyMYqTiPNDtNd1Y2A2ECkemuuS7jsGc8szyJjhmrzMgLQ7XmzeY3F1XQWYQ302vI8AjGu7uZgg83Wy4hy5TlRZ_eIAByHduB7DfRUg2WzmBDcqID7z_dN_wLd3mzVl6i-WqzlK3QruVxNlosmOmDjsGmw_wsAXs9T |
| 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=SAKM+%3A+Self-adaptive+kernel+machine+A+kernel-based+algorithm+for+online+clustering&rft.jtitle=Neural+networks&rft.au=AMADOU+BOUBACAR%2C+Habiboulaye&rft.au=LECOEUCHE%2C+St%C3%A9phane&rft.au=MAOUCHE%2C+Salah&rft.date=2008-11-01&rft.pub=Elsevier&rft.issn=0893-6080&rft.volume=21&rft.issue=9&rft.spage=1287&rft.epage=1301&rft_id=info:doi/10.1016%2Fj.neunet.2008.03.016&rft.externalDBID=n%2Fa&rft.externalDocID=20887283 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0893-6080&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0893-6080&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0893-6080&client=summon |