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Related Experiment Videos

Learning Hidden Markov Models for Regression using Path Aggregation.

Keith Noto1, Mark Craven

  • 1Dept. of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093.

Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hidden Markov model (HMM) for regression, effectively mapping sequential data to continuous responses. Jointly learning HMM structure and regression models improves performance in synthetic and biological applications.

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Area of Science:

  • Machine Learning
  • Computational Biology
  • Statistical Modeling

Background:

  • Sequential data analysis is crucial in various fields.
  • Hidden Markov Models (HMMs) are powerful for sequence modeling.
  • Integrating regression with sequence models presents challenges.

Purpose of the Study:

  • To develop and evaluate an approach for learning HMMs for regression tasks.
  • To jointly learn the structure and parameters of an HMM with a regression model.
  • To map sequential data features to continuous-valued responses.

Main Methods:

  • Proposed a novel approach for learning hidden Markov models (HMMs) for regression.
  • Simultaneously inferred HMM structure and parameters.
  • Developed a regression model mapping path features to continuous responses.

Main Results:

  • Demonstrated the effectiveness of the joint learning approach.
  • Achieved successful results in both synthetic datasets and biological domains.
  • Validated the value of integrating HMM learning with regression.

Conclusions:

  • Jointly learning HMMs and regression models is a valuable approach for sequential data.
  • The proposed method offers a robust framework for regression on sequential data.
  • This technique has potential applications in biological sequence analysis and beyond.