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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Communication dans un congrès
IAPR Workshop on Partially Supervised Learning (PSL), Sep 2011, Ulm, Germany. Springer, IAPR Workshop on Partially Supervised Learning (PSL), 7081, pp.92-95, Lecture Notes in Computer Science. 〈10.1007/978-3-642-28258-4_10〉
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https://hal.archives-ouvertes.fr/hal-01286786
Contributeur : Lip6 Publications <>
Soumis le : vendredi 11 mars 2016 - 13:52:24
Dernière modification le : mercredi 21 mars 2018 - 18:58:10

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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. Springer, IAPR Workshop on Partially Supervised Learning (PSL), 7081, pp.92-95, Lecture Notes in Computer Science. 〈10.1007/978-3-642-28258-4_10〉. 〈hal-01286786〉

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