Iterative refinement of HMM and HCRF for sequence classification

Authors:
Yann Soullard;Thierry Artieres
Affiliations:
LIP6, Pierre and Marie Curie University, Paris, France;LIP6, Pierre and Marie Curie University, Paris, France
Venue:
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Year:
2011

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Abstract

We propose a strategy for semi-supervised learning of Hidden-state Conditional Random Fields (HCRF) for signal classification. It builds on simple procedures for semi-supervised learning of Hidden Markov Models (HMM) and on strategies for learning a HCRF from a trained HMM system. The algorithm learns a generative system based on Hidden Markov models and a discriminative one based on HCRFs where each model is refined by the other in an iterative framework.