@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.} }