Learning Hidden Markov Models from the state distribution oracle

Document Type

Conference Proceeding

Publication Date

12-1-2004

Abstract

A Hidden Markov Model (HMM) is a probabilistic model that has been widely applied to a number of fields since its inception over 30 years ago. Computational Biology, Speech Recognition, and Image Processing are but a few of the application areas of HMMs. We propose an efficient algorithm for learning the parameters of a first order HMM from a state distribution (SD) oracle. The SD oracle provides the learner with the state distribution vector corresponding to a query string in the model. The SD oracle is shown to be necessary for polynomial-time learning in the sense that the consistency problem involving learning HMM parameters from a training set of state distribution vectors without the ability to query the SD oracle, is NP-complete. The learning algorithm proposed is based on an algorithm described by Tzeng for learning Probabilistic Automata. ©2004 IEEE.

Publication Source (Journal or Book title)

Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04

First Page

73

Last Page

80

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