Learning Hidden Markov Models for Regression Using Path Aggregation
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.
Cite
Text
Noto and Craven. "Learning Hidden Markov Models for Regression Using Path Aggregation." Conference on Uncertainty in Artificial Intelligence, 2008.Markdown
[Noto and Craven. "Learning Hidden Markov Models for Regression Using Path Aggregation." Conference on Uncertainty in Artificial Intelligence, 2008.](https://mlanthology.org/uai/2008/noto2008uai-learning/)BibTeX
@inproceedings{noto2008uai-learning,
title = {{Learning Hidden Markov Models for Regression Using Path Aggregation}},
author = {Noto, Keith and Craven, Mark},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2008},
pages = {444-451},
url = {https://mlanthology.org/uai/2008/noto2008uai-learning/}
}