Proceedings

A Hidden Markov Model Variant for Sequence Classification

Sam Blasiak, Huzefa Rangwala

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1192-1196.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-203

Abstract

Sequence classification is central to many practical problems within machine learning. Distances metrics between arbitrary pairs of sequences can be hard to define because sequences can vary in length and the information contained in the order of sequence elements is lost when standard metrics such as Euclidean distance are applied. We present a scheme that employs a Hidden Markov Model variant to produce a set of fixed-length description vectors from a set of sequences. We then define three inference algorithms, a Baum-Welch variant, a Gibbs Sampling algorithm, and a variational algorithm, to infer model parameters. Finally, we show experimentally that the fixed length representation produced by these inference methods is useful for classifying sequences of amino acids into structural classes

Sam Blasiak, and Huzefa Rangwala. 2011. A Hidden Markov Model Variant for Sequence Classification. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1192–1196. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-203
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