DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, HC | - |
dc.contributor.author | Pang, S | - |
dc.contributor.author | Je, HM | - |
dc.contributor.author | Kim, D | - |
dc.contributor.author | Bang, SY | - |
dc.date.accessioned | 2016-03-31T12:41:10Z | - |
dc.date.available | 2016-03-31T12:41:10Z | - |
dc.date.created | 2009-03-19 | - |
dc.date.issued | 2002-01 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.other | 2004-OAK-0000003882 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/18199 | - |
dc.description.abstract | Even the support vector machine (SVM) has been proposed to provide a good generalization performance, the classification result of the practically implemented SVM is often far from the theoretically expected level because their implementations are based on the approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use the SVM ensembles with bagging (bootstrap aggregating). Each individual SVM is trained independently using the randomly chosen training samples via a bootstrap technique. Then, they are aggregated into to make a collective decision in several ways such as the majority voting, the LSE(least squares estimation)-based weighting, and the double-layer hierarchical combining. Various simulation results for the IRIS data classification and the hand-written digit recognitionshow that the proposed SVM ensembles with bagging outperforms a single SVM in terms of classification accuracy greatly. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.relation.isPartOf | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.title | Support vector machine ensemble with bagging | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1007/3-540-45665-1_31 | - |
dc.author.google | Kim, HC | - |
dc.author.google | Pang, S | - |
dc.author.google | Je, HM | - |
dc.author.google | Kim, D | - |
dc.author.google | Bang, SY | - |
dc.relation.volume | 2388 | - |
dc.relation.startpage | 397 | - |
dc.relation.lastpage | 407 | - |
dc.contributor.id | 10054411 | - |
dc.relation.journal | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Conference Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | LECTURE NOTES IN COMPUTER SCIENCE, v.2388, pp.397 - 407 | - |
dc.identifier.wosid | 000187252200031 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 407 | - |
dc.citation.startPage | 397 | - |
dc.citation.title | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.citation.volume | 2388 | - |
dc.contributor.affiliatedAuthor | Kim, D | - |
dc.identifier.scopusid | 2-s2.0-84958774749 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 26 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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