Combination of multiple classifiers by minimizing the upper bound of bayes error rate for unconstrained handwritten numeral recognition
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, HJ | - |
dc.contributor.author | Lee, SW | - |
dc.date.accessioned | 2021-09-09T06:52:19Z | - |
dc.date.available | 2021-09-09T06:52:19Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2005-05 | - |
dc.identifier.issn | 0218-0014 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/123239 | - |
dc.description.abstract | In order to raise a class discrimination power by the combination of multiple classifiers, the upper bound of Bayes error rate which is bounded by the conditional entropy of a class and decisions should be minimized. Based on the minimization of the upper bound of the Bayes error rate, Wang and Wong proposed only a tree dependence approximation scheme of a high-dimensional probability distribution composed of a class and patterns. This paper extends such a tree dependence approximation scheme to higher order dependency for improving the classification performance and thus optimally approximates the high-dimensional probability distribution with a product of low-dimensional distributions. And then, a new combination method by the proposed approximation scheme is presented and evaluated with classifiers recognizing unconstrained handwritten numerals. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WORLD SCIENTIFIC PUBL CO PTE LTD | - |
dc.subject | PROBABILITY | - |
dc.subject | CLASSIFICATION | - |
dc.subject | EXPERTS | - |
dc.title | Combination of multiple classifiers by minimizing the upper bound of bayes error rate for unconstrained handwritten numeral recognition | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, SW | - |
dc.identifier.doi | 10.1142/S0218001405004101 | - |
dc.identifier.scopusid | 2-s2.0-18644367804 | - |
dc.identifier.wosid | 000229851200006 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.19, no.3, pp.395 - 413 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | - |
dc.citation.title | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | - |
dc.citation.volume | 19 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 395 | - |
dc.citation.endPage | 413 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | PROBABILITY | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | EXPERTS | - |
dc.subject.keywordAuthor | combination of multiple classifiers | - |
dc.subject.keywordAuthor | Bayes error rate | - |
dc.subject.keywordAuthor | dependency | - |
dc.subject.keywordAuthor | approximation scheme | - |
dc.subject.keywordAuthor | mutual information | - |
dc.subject.keywordAuthor | handwritten numeral recognition | - |
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