Learning a Hybrid Architecture for Sequence Regression and Annotation

Authors

  • Yizhe Zhang Duke University
  • Ricardo Henao Duke University
  • Lawrence Carin Duke University
  • Jianling Zhong Duke University
  • Alexander Hartemink Duke University

DOI:

https://doi.org/10.1609/aaai.v30i1.10145

Abstract

When learning a hidden Markov model (HMM), sequential observations can often be complemented by real-valued summary response variables generated from the path of hidden states. Such settings arise in numerous domains, including many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible framework for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is compatible with a rich set of mapping functions. Results show that the availability of additional continuous response variables can simultaneously improve the annotation of the sequential observations and yield good prediction performance in both synthetic data and real-world datasets.

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Published

2016-02-21

How to Cite

Zhang, Y., Henao, R., Carin, L., Zhong, J., & Hartemink, A. (2016). Learning a Hybrid Architecture for Sequence Regression and Annotation. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10145

Issue

Section

Technical Papers: Machine Learning Applications