DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, HJ | ko |
dc.contributor.author | Yun, SJ | ko |
dc.contributor.author | Oh, Yung-Hwan | ko |
dc.date.accessioned | 2010-03-22T09:04:59Z | - |
dc.date.available | 2010-03-22T09:04:59Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1998-07 | - |
dc.identifier.citation | ARTIFICIAL INTELLIGENCE IN ENGINEERING, v.12, no.3, pp.243 - 252 | - |
dc.identifier.issn | 0954-1810 | - |
dc.identifier.uri | http://hdl.handle.net/10203/17282 | - |
dc.description.abstract | We propose a new variant of the discrete hidden Markov model (DHMM) in which the output distribution is estimated by state-dependent source quantizing modeling and the output probability is weighted by the entropy of each feature-parameter at a state. The state-dependent source is represented as a state-dependent quantized vector which is regarded as a variant of a representative vector at a state and its own codeword distribution, and the output distribution is derived by these state-dependent sources which will exist at a state. In addition, entropy-based feature-parameter weighting is proposed to reflect the different importance of each feature-parameter in a state, and the fuzzy function is applied to transform an entropy value into a feature-parameter weighting factor. From experiments, we found that proposed methods have shown an improvement of 5.6%, which indicates the effectiveness of proposed models in the robust estimation of output probabilities for DHMMs. (C) 1998 Elsevier Science Limited. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | ELSEVIER SCI LTD | - |
dc.subject | WORD RECOGNITION | - |
dc.title | Robust estimation of discrete hidden Markov model parameters using the entropy-based feature-parameter weighting and source-quantization modeling | - |
dc.type | Article | - |
dc.identifier.wosid | 000073659600010 | - |
dc.identifier.scopusid | 2-s2.0-0032122176 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 243 | - |
dc.citation.endingpage | 252 | - |
dc.citation.publicationname | ARTIFICIAL INTELLIGENCE IN ENGINEERING | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Oh, Yung-Hwan | - |
dc.contributor.nonIdAuthor | Choi, HJ | - |
dc.contributor.nonIdAuthor | Yun, SJ | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | hidden Markov model | - |
dc.subject.keywordAuthor | state-dependent source quantization modeling | - |
dc.subject.keywordAuthor | entropy | - |
dc.subject.keywordAuthor | feature-parameter weighting | - |
dc.subject.keywordAuthor | fuzzy objective function | - |
dc.subject.keywordPlus | WORD RECOGNITION | - |
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