Online Learning in Discrete Hidden Markov Models
Machine Learning
2007-08-20 v1
Abstract
We present and analyse three online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare them with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalisation error we draw learning curves in simplified situations. The performance for learning drifting concepts of one of the presented algorithms is analysed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented.
Keywords
Cite
@article{arxiv.0708.2377,
title = {Online Learning in Discrete Hidden Markov Models},
author = {Roberto C. Alamino and Nestor Caticha},
journal= {arXiv preprint arXiv:0708.2377},
year = {2007}
}
Comments
8 pages, 6 figures