PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Large Scale Semi Hidden Markov SVMs
Gunnar Raetsch and Sören Sonnenburg
In: NIPS 2006, 3 - 9 Dec 2006, Vancouver, CA.

Abstract

We describe Hidden Semi-Markov Support Vector Machines (SHM SVMs), an extension of HM SVMs to semi-Markov chains. This allows us to predict seg- mentations of sequences based on segment-based features measuring properties such as the length of the segment. We propose a novel technique to partition the problem into sub-problems. The independently obtained partial solutions can then be recombined in an efficient way, which allows us to solve label sequence learn- ing problems with several thousands of labeled sequences. We have tested our algorithm for predicting gene structures, an important problem in computational biology. Results on a well-known model organism illustrate the great potential of SHM SVMs in computational biology.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3125
Deposited By:Gunnar Raetsch
Deposited On:21 December 2007