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Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging

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Abstract

The aim of multitask learning is to improve the generalization performance of a set of related tasks by exploiting complementary information about the tasks. In this paper, we review established approaches for regularization based multitask learning, sketch some recent developments, and demonstrate their applications in Computational Biology and Biological Imaging.

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Acknowledgments

We thank Klaus-Robert Müller and Mehryar Mohri for inspiring and helpful discussions. This work was supported by the German Research Foundation (DFG) under MU 987/6-1 and RA 1894/1-1 as well as by the European Community’s 7th Framework Programme under the PASCAL2 Network of Excellence (ICT-216886). Marius Kloft acknowledges a postdoctoral fellowship by the German Research Foundation (DFG). We also thank Anna-Katerina Hadjantonakis and Minjung Kang for the 3D image data.

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Authors and Affiliations

  1. Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 415 E 68th Street, New York, NY, 10065, USA

    Christian Widmer, Marius Kloft, Xinghua Lou & Gunnar Rätsch

  2. Machine Learning Group, Technische Universität Berlin, Marchstr. 23, 10587, Berlin, Germany

    Christian Widmer

  3. Courant Institute of Mathematical Sciences, 251 Mercer St, New York, NY, 10012, USA

    Marius Kloft

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  1. Christian Widmer
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  2. Marius Kloft
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  3. Xinghua Lou
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  4. Gunnar Rätsch
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Correspondence to Gunnar Rätsch.

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Widmer, C., Kloft, M., Lou, X. et al. Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging. Künstl Intell 28, 29–33 (2014). https://doi.org/10.1007/s13218-013-0283-y

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  • DOI: https://doi.org/10.1007/s13218-013-0283-y

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