Factorial Hidden Markov Models

Factorial Hidden Markov Models

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,...

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Bibliographic Details
Main Authors: Ghahramani, Zoubin, Jordan, Michael I.
Language:en_US
Published: 2004
Subjects:
AI
MIT
Artificial Intelligence
Hidden Markov Models
sNeural networks
Time series
Mean field theory
Gibbs sampling
sFactorial
Learning algorithms
Machine learning
Online Access:http://hdl.handle.net/1721.1/7188

Internet

http://hdl.handle.net/1721.1/7188

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