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  Efficient Training of Graph-Regularized Multitask SVMs

Widmer, C., Kloft, M., Görnitz, N., & Rätsch, G. (2012). Efficient Training of Graph-Regularized Multitask SVMs. In P. Flach, T. De Bie, & N. Cristianini (Eds.), Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2012 (pp. 633-647). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-8B50-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-8B51-7
Genre: Conference Paper

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 Creators:
Widmer, C1, Author              
Kloft, M, Author
Görnitz, N, Author
Rätsch, G1, Author              
Flach, PA, Editor
Bie, T De, Editor
Cristianini, N, Editor
Affiliations:
1FML, Max-Planck Society, Tübingen, Germany, ou_persistent22              

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 Abstract: We present an optimization framework for graph-regularized multi-task SVMs based on the primal formulation of the problem. Previous approaches employ a so-called multi-task kernel (MTK) and thus are inapplicable when the numbers of training examples n is large (typically n < 20,000, even for just a few tasks). In this paper, we present a primal optimization criterion, allowing for general loss functions, and derive its dual representation. Building on the work of Hsieh et al. [1,2], we derive an algorithm for optimizing the large-margin objective and prove its convergence. Our computational experiments show a speedup of up to three orders of magnitude over LibSVM and SVMLight for several standard benchmarks as well as challenging data sets from the application domain of computational biology. Combining our optimization methodology with the COFFIN large-scale learning framework [3], we are able to train a multi-task SVM using over 1,000,000 training points stemming from 4 different tasks. An efficient C++ implementation of our algorithm is being made publicly available as a part of the SHOGUN machine learning toolbox [4].

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 Dates: 2012-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1007/978-3-642-33460-3_46
BibTex Citekey: WidmerKGRTN2012
 Degree: -

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Title: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2012)
Place of Event: Bristol, UK
Start-/End Date: 2012-09-24 - 2012-09-28

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Title: Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2012
Source Genre: Proceedings
 Creator(s):
Flach, PA, Editor
De Bie, T, Editor
Cristianini, N, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 633 - 647 Identifier: ISBN: 978-3-642-33459-7

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 7523 Sequence Number: - Start / End Page: - Identifier: -