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Abstract: 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 ... (Update)
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BibTeX entry: (Update)
Zoubin Ghahramani and Michael I. Jordan. Factorial hidden Markov models. In David S. Touretzky, Michael C. Mozer, and M.E. Hasselmo, editors, NIPS, volume 8, Cambridge, MA, 1996. MITP. http://citeseer.ist.psu.edu/ghahramani96factorial.html More
@inproceedings{ ghahramani95factorial,
author = "Zoubin Ghahramani and Michael I. Jordan",
title = "Factorial Hidden {M}arkov Models",
booktitle = "Proc. Conf. Advances in Neural Information Processing Systems, {NIPS}",
volume = "8",
publisher = "MIT Press",
editor = "David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo",
isbn = "0262201070",
pages = "472--478",
year = "1995",
url = "citeseer.ist.psu.edu/ghahramani96factorial.html" }
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