DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, D | - |
dc.contributor.author | Lee, J | - |
dc.date.accessioned | 2016-04-01T01:42:13Z | - |
dc.date.available | 2016-04-01T01:42:13Z | - |
dc.date.created | 2009-04-01 | - |
dc.date.issued | 2007-03 | - |
dc.identifier.issn | 1045-9227 | - |
dc.identifier.other | 2007-OAK-0000006670 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/23517 | - |
dc.description.abstract | A novel learning algorithm for semisupervised classification is proposed. The proposed method first constructs a support function that estimates a support of a data distribution using both labeled and unlabeled data. Then, it partitions a whole data space into a small number of disjoint regions with the aid of a dynamical system. Finally, it labels the decomposed regions utilizing the labeled data and the cluster structure described by the constructed support function. Simulation results show the effectiveness of the proposed method to label out-of-sample unlabeled test data as well as in-sample unlabeled data. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGI | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON NEURAL NETWORKS | - |
dc.subject | dynamical systems | - |
dc.subject | inductive learning | - |
dc.subject | kernel methods | - |
dc.subject | semisupervised learning | - |
dc.subject | support vector machines (SVMs) | - |
dc.title | Equilibrium-based support vector machine for semisupervised classification | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1109/TNN.2006.889 | - |
dc.author.google | Lee, D | - |
dc.author.google | Lee, J | - |
dc.relation.volume | 18 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 578 | - |
dc.relation.lastpage | 583 | - |
dc.contributor.id | 10081901 | - |
dc.relation.journal | IEEE TRANSACTIONS ON NEURAL NETWORKS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON NEURAL NETWORKS, v.18, no.2, pp.578 - 583 | - |
dc.identifier.wosid | 000244970900020 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 583 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 578 | - |
dc.citation.title | IEEE TRANSACTIONS ON NEURAL NETWORKS | - |
dc.citation.volume | 18 | - |
dc.contributor.affiliatedAuthor | Lee, J | - |
dc.identifier.scopusid | 2-s2.0-34047146595 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 41 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | dynamical systems | - |
dc.subject.keywordAuthor | inductive learning | - |
dc.subject.keywordAuthor | kernel methods | - |
dc.subject.keywordAuthor | semisupervised learning | - |
dc.subject.keywordAuthor | support vector machines (SVMs) | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
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