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.
Document type :
Conference papers
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01286786
Contributor : Lip6 Publications <>
Submitted on : Friday, March 11, 2016 - 1:52:24 PM
Last modification on : Thursday, March 21, 2019 - 2:14:32 PM

Links full text

Identifiers

Citation

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⟩

Share

Metrics

Record views

178