Presentation 2002/10/10
Elastic Hidden Markov Model and Its Application to Sequence Classification
Tsuyoshi KATO, Shinichiro OMACHI, Hirotomo ASO,
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Abstract(in English) In this paper, we describe a new probabilistic sequence model, elastic hidden Markov model(EHMM). The most popular model used to model sequential patterns is the hidden Markov model(HMM). A major shortcoming of the HMM is the assumption that implies that all observations are only dependent on the state generating them, not on neighboring observations. To cope with this problem, the EHMM represents the correlation between observations by adapting the model parameters to an observed sequence on Bayesian framework. Finally, we use EHMMs to model online digit patterns and show that EHMMs can capture the correlation structure in this data set.
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Keyword(in English) elastic hidden Markov model / latent variable model / variational method / sequence classification / online character recognition
Paper # NC2002-47
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Committee NC
Conference Date 2002/10/10(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Elastic Hidden Markov Model and Its Application to Sequence Classification
Sub Title (in English)
Keyword(1) elastic hidden Markov model
Keyword(2) latent variable model
Keyword(3) variational method
Keyword(4) sequence classification
Keyword(5) online character recognition
1st Author's Name Tsuyoshi KATO
1st Author's Affiliation Graduate School of Engineering, Tohoku University()
2nd Author's Name Shinichiro OMACHI
2nd Author's Affiliation Graduate School of Engineering, Tohoku University
3rd Author's Name Hirotomo ASO
3rd Author's Affiliation Graduate School of Engineering, Tohoku University
Date 2002/10/10
Paper # NC2002-47
Volume (vol) vol.102
Number (no) 381
Page pp.pp.-
#Pages 6
Date of Issue