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Factorial Hidden Markov Models

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dc.creator Ghahramani, Zoubin
dc.creator Jordan, Michael I.
dc.date 2004-10-20T20:49:14Z
dc.date 2004-10-20T20:49:14Z
dc.date 1996-02-09
dc.date.accessioned 2013-10-09T02:48:31Z
dc.date.available 2013-10-09T02:48:31Z
dc.date.issued 2013-10-09
dc.identifier AIM-1561
dc.identifier CBCL-130
dc.identifier http://hdl.handle.net/1721.1/7188
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.
dc.format 7 p.
dc.format 198365 bytes
dc.format 244196 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-1561
dc.relation CBCL-130
dc.subject AI
dc.subject MIT
dc.subject Artificial Intelligence
dc.subject Hidden Markov Models
dc.subject sNeural networks
dc.subject Time series
dc.subject Mean field theory
dc.subject Gibbs sampling
dc.subject sFactorial
dc.subject Learning algorithms
dc.subject Machine learning
dc.title Factorial Hidden Markov Models


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