@incollection{RaeSon07,
	title = {Large Scale Hidden Semi-Markov SVMs},
	author = {Gunnar R\"{a}tsch and S\"{o}ren Sonnenburg},
	booktitle = {Advances in Neural Information Processing Systems 19},
	editor = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
	publisher = {MIT Press},
	address = {Cambridge, MA},
	pages = {1161--1168},
	year = {2007},
	ps =  {http://books.nips.cc/papers/files/nips19/NIPS2006_0151.ps.gz},
	pdf =  {http://books.nips.cc/papers/files/nips19/NIPS2006_0151.pdf},
	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.}
}