DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, H | - |
dc.contributor.author | Oh, JH | - |
dc.date.accessioned | 2016-03-31T13:48:40Z | - |
dc.date.available | 2016-03-31T13:48:40Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 1998-09-25 | - |
dc.identifier.issn | 0305-4470 | - |
dc.identifier.other | 1998-OAK-0000000433 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/20630 | - |
dc.description.abstract | We study learning from examples by higher-order perceptrons, which realize polynomially separable rules. The model complexities of the networks are made 'tunable' by varying the relative orders of different monomial terms. We analyse the learning curves of higher-order perceptrons when the Gibbs algorithm is used for training. It is found that learning occurs in a stepwise manner. This is because the number of examples needed to constrain the corresponding phase-space component scales differently. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | IOP PUBLISHING LTD | - |
dc.relation.isPartOf | JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL | - |
dc.subject | NEURAL NETWORKS | - |
dc.subject | STATISTICAL-MECHANICS | - |
dc.subject | EXAMPLES | - |
dc.title | Learning of higher-order perceptrons with tunable complexities | - |
dc.type | Article | - |
dc.contributor.college | 기술경영 대학원 과정 | - |
dc.identifier.doi | 10.1088/0305-4470/31/38/012 | - |
dc.author.google | YOON, H | - |
dc.author.google | OH, JH | - |
dc.relation.volume | 31 | - |
dc.relation.issue | 38 | - |
dc.relation.startpage | 7771 | - |
dc.relation.lastpage | 7784 | - |
dc.contributor.id | 10110134 | - |
dc.relation.journal | JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, v.31, no.38, pp.7771 - 7784 | - |
dc.identifier.wosid | 000076288300012 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 7784 | - |
dc.citation.number | 38 | - |
dc.citation.startPage | 7771 | - |
dc.citation.title | JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL | - |
dc.citation.volume | 31 | - |
dc.contributor.affiliatedAuthor | Oh, JH | - |
dc.identifier.scopusid | 2-s2.0-0032566578 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 7 | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | NEURAL NETWORKS | - |
dc.subject.keywordPlus | STATISTICAL-MECHANICS | - |
dc.subject.keywordPlus | EXAMPLES | - |
dc.relation.journalWebOfScienceCategory | Physics, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Mathematical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Physics | - |
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