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Conference Paper: Higher-order hidden markov models with applications to DNA sequences

TitleHigher-order hidden markov models with applications to DNA sequences
Authors
Ching, WKFung, ESNg, MK
Issue Date2003
PublisherSpringer.
Citation
4th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2003), Hong Kong, 21-23 March 2003. In Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003, Hong Kong, China, March 21-23, 2003: Revised Papers, 2003, p. 535-539 How to Cite?
DOI: http://dx.doi.org/10.1007/978-3-540-45080-1_73
AbstractHidden Markov models (HMMs) have been applied to many real-world applications. Very often HMMs only deal with the first order transition probability distribution among the hidden states. In this paper we develop higher-order HMMs. We study the evaluation of the probability of a sequence of observations based on higher-order HMMs and determination of a best sequence of model states. © Springer-Verlag 2003.
Persistent Identifierhttp://hdl.handle.net/10722/123597
ISBN
9783540405504
ISSN
0302-9743
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 2690
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorFung, ESen_HK
dc.contributor.authorNg, MKen_HK
dc.date.accessioned2010-09-26T12:15:10Z-
dc.date.available2010-09-26T12:15:10Z-
dc.date.issued2003en_HK
dc.identifier.citation4th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2003), Hong Kong, 21-23 March 2003. In Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003, Hong Kong, China, March 21-23, 2003: Revised Papers, 2003, p. 535-539en_HK
dc.identifier.isbn9783540405504-
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/123597-
dc.description.abstractHidden Markov models (HMMs) have been applied to many real-world applications. Very often HMMs only deal with the first order transition probability distribution among the hidden states. In this paper we develop higher-order HMMs. We study the evaluation of the probability of a sequence of observations based on higher-order HMMs and determination of a best sequence of model states. © Springer-Verlag 2003.en_HK
dc.languageengen_HK
dc.publisherSpringer.-
dc.relation.ispartofIntelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003, Hong Kong, China, March 21-23, 2003: Revised Papersen_HK
dc.relation.ispartofseriesLecture Notes in Computer Science ; 2690-
dc.titleHigher-order hidden markov models with applications to DNA sequencesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-45080-1_73-
dc.identifier.scopuseid_2-s2.0-35048898011en_HK
dc.identifier.hkuros88727en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-35048898011&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage535en_HK
dc.identifier.epage539en_HK
dc.publisher.placeBerlin-
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridFung, ES=7005440799en_HK
dc.identifier.scopusauthoridNg, MK=7202076432en_HK
dc.identifier.issnl0302-9743-

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