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Learning Hidden Markov Models for Regression using Path Aggregation

We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for regression. The learning process involves inferring the structure and parameters of a conventional HMM, while simultaneousl...

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Détails bibliographiques
Auteurs principaux: Noto, Keith, Craven, Mark
Format: Artigo
Langue:Inglês
Publié: 2008
Sujets:
Accès en ligne:https://ncbi.nlm.nih.gov/pmc/articles/PMC3141580/
https://ncbi.nlm.nih.gov/pubmed/21785575
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