PAC-learning of Markov models with hidden state
Ricard Gavaldà, Philipp W. Keller, Joelle Pineau and Doina Precup
In: 17th European Conference on Machine Learning (ECML'06), 18-22 Sep 2006, Berlin, Germany.
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical applications (e.g. speech recognition, biological sequence alignment) it has two major limitations: it requires a known model topology, and learning is only locally optimal. We propose a new PAC framework for learning both the topology and the parameters in partially observable Markov models. Our algorithm learns a Probabilistic Deterministic Finite Automata (PDFA) which approximates a Hidden Markov Model (HMM) up to some desired degree of accuracy. We discuss theoretical conditions under which the algorithm produces an optimal solution (in the PAC-sense) and demonstrate promising performance on simple dynamical systems.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Also available for browsing from the author's homepage, subject to the copyright restrictions. |
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Learning/Statistics & Optimisation|
Theory & Algorithms
|Deposited By:||Ricard Gavaldà|
|Deposited On:||23 November 2006|