Scalable Hierarchical Multitask Learning in Sequence Biology
Christian Widmer, Jose Leiva-Murillo, Yasemin Altun and Gunnar Raetsch
In: Machine Learning for Computational Biology Workshop 2009, 11 Dec 2009, Whistler, Canada.

Abstract

Multitask learning methods investigate the challenge of combining information from several related problem domains. For a large family of multitask problems, relationships between tasks can be described by a hierarchical structure. This is particularly the case for many problems in Computational Biology, where differ- ent tasks correspond to different organisms, whose relationship to each other is defined by phylogeny. In this work we describe a set of algorithms that com- bine large-scale classification methods with ideas from Domain Adaptation and Multitask Learning. We introduce several methods of exploiting hierarchical task relations for Multitask Learning. These algorithms are designed for large-scale applications and scale to problems with a great number of training examples. The performance of the presented methods is demonstrated on synthetic data, as well as on splice-site data arising from a problem in genomic sequence analysis.

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