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Iterative Refinement of HMM and HCRF for Sequence Classification

yann Soullard 1 Thierry Artières 1 
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
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.
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Submitted on : Friday, March 11, 2016 - 1:52:24 PM
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yann Soullard, Thierry Artières. Iterative Refinement of HMM and HCRF for Sequence Classification. IAPR Workshop on Partially Supervised Learning (PSL), Sep 2011, Ulm, Germany. pp.92-95, ⟨10.1007/978-3-642-28258-4_10⟩. ⟨hal-01286786⟩



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