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Communication Dans Un Congrès Année : 2011

Iterative Refinement of HMM and HCRF for Sequence Classification

Résumé

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.

Dates et versions

hal-01286786 , version 1 (11-03-2016)

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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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