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