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Article: A method to speed up the Bayes classifier

TitleA method to speed up the Bayes classifier
Authors
KeywordsBayes classifier
Branch and bound algorithm
Character recognition
Chinese characters
K-nn rule
Nearest neighbour
Pattern recognition
Issue Date1998
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappai
Citation
Engineering Applications Of Artificial Intelligence, 1998, v. 11 n. 3, p. 419-424 How to Cite?
AbstractA method is proposed to combine the branch-and-bound (BAB) algorithm with the Bayes classifier. Given the input feature vector from an unknown class, the BAB algorithm is efficient for searching for the nearest neighbor (NN) from among the set of reference vectors. Hence BAB is often used to implement the k-NN classifier. However, it is known that the k-NN classifier is not as accurate as the Bayes classifier, which has the highest recognition rate provided the class statistics are known. Hence it is attractive to combine the BAB algorithm with the Bayes classifier so that the resulting system will inherit improved speed and accuracy. In this article, an extension of the BAB algorithm is proposed so that it can be used to implement the Bayes classifier. Gaussian statistics are assumed in modeling the class conditional densities. A system for recognizing printed Chinese characters is implemented, and satisfactory results are obtained. © 1998 Elsevier Science Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73920
ISSN
2015 Impact Factor: 2.368
2015 SCImago Journal Rankings: 1.371
References

 

DC FieldValueLanguage
dc.contributor.authorLeung, CHen_HK
dc.contributor.authorSze, Len_HK
dc.date.accessioned2010-09-06T06:56:03Z-
dc.date.available2010-09-06T06:56:03Z-
dc.date.issued1998en_HK
dc.identifier.citationEngineering Applications Of Artificial Intelligence, 1998, v. 11 n. 3, p. 419-424en_HK
dc.identifier.issn0952-1976en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73920-
dc.description.abstractA method is proposed to combine the branch-and-bound (BAB) algorithm with the Bayes classifier. Given the input feature vector from an unknown class, the BAB algorithm is efficient for searching for the nearest neighbor (NN) from among the set of reference vectors. Hence BAB is often used to implement the k-NN classifier. However, it is known that the k-NN classifier is not as accurate as the Bayes classifier, which has the highest recognition rate provided the class statistics are known. Hence it is attractive to combine the BAB algorithm with the Bayes classifier so that the resulting system will inherit improved speed and accuracy. In this article, an extension of the BAB algorithm is proposed so that it can be used to implement the Bayes classifier. Gaussian statistics are assumed in modeling the class conditional densities. A system for recognizing printed Chinese characters is implemented, and satisfactory results are obtained. © 1998 Elsevier Science Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappaien_HK
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_HK
dc.subjectBayes classifieren_HK
dc.subjectBranch and bound algorithmen_HK
dc.subjectCharacter recognitionen_HK
dc.subjectChinese charactersen_HK
dc.subjectK-nn ruleen_HK
dc.subjectNearest neighbouren_HK
dc.subjectPattern recognitionen_HK
dc.titleA method to speed up the Bayes classifieren_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0952-1976&volume=11&spage=419&epage=424&date=1998&atitle=A+Method+to+Speed+Up+the+Bayes+Classifieren_HK
dc.identifier.emailLeung, CH:chleung@eee.hku.hken_HK
dc.identifier.authorityLeung, CH=rp00146en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0952-1976(98)00006-2-
dc.identifier.scopuseid_2-s2.0-0032090623en_HK
dc.identifier.hkuros45399en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0032090623&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume11en_HK
dc.identifier.issue3en_HK
dc.identifier.spage419en_HK
dc.identifier.epage424en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLeung, CH=7402612415en_HK
dc.identifier.scopusauthoridSze, L=6602158907en_HK

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