Learning hidden Markov models for regression using path aggregation

Keith Noto, Mark Craven
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:444-451, 2008.

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 this Paper

BibTeX
@InProceedings{pmlr-vR6-noto08a, title = {Learning hidden Markov models for regression using path aggregation}, author = {Noto, Keith and Craven, Mark}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {444--451}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/noto08a/noto08a.pdf}, url = {https://proceedings.mlr.press/r6/noto08a.html}, 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.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Learning hidden Markov models for regression using path aggregation %A Keith Noto %A Mark Craven %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-noto08a %I PMLR %P 444--451 %U https://proceedings.mlr.press/r6/noto08a.html %V R6 %X 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. %Z Reissued by PMLR on 09 October 2024.
APA
Noto, K. & Craven, M.. (2008). Learning hidden Markov models for regression using path aggregation. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:444-451 Available from https://proceedings.mlr.press/r6/noto08a.html. Reissued by PMLR on 09 October 2024.

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