Learning Hidden Markov Models for Regression using Path Aggregation

Keith Noto; Mark Craven

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arXiv · 1206.3275

Learning Hidden Markov Models for Regression using Path Aggregation

Keith Noto·Mark Craven

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Abstract

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 simultaneously learning a regression model that maps features that characterize paths through the model to continuous responses. Our results, in both synthetic and biological domains, demonstrate the value of jointly learning the two components of our approach.

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BibTeX RIS

Keith Noto, Mark Craven. 2012-06-13. Learning Hidden Markov Models for Regression using Path Aggregation. https://arxiv.org/abs/1206.3275

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