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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7188Full metadata record
| DC Field | Value | Language |
|---|---|---|
| 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 | - |
| Appears in Collections: | MIT Items | |
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