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  Fast Binary and Multi-Output Reduced Set Selection

Weston, J., & Bakir, G.(2004). Fast Binary and Multi-Output Reduced Set Selection (132). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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MPIK-TR-132.pdf (Publisher version), 156KB
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Weston, J, Author           
Bakir, GH1, 2, Author           
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We propose fast algorithms for reducing the number of kernel evaluations in the testing phase for methods such as Support Vector Machines (SVM) and Ridge Regression (RR). For non-sparse methods such as RR this results in significantly improved prediction time. For binary SVMs, which are already sparse in their expansion, the pay off is mainly in the cases of noisy or large-scale problems. However, we then further develop our method for multi-class problems where, after choosing the expansion to find vectors which describe all the hyperplanes jointly, we again achieve significant gains.

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 Dates: 2004-11
 Publication Status: Published in print
 Pages: 12
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 132
BibTex Citekey: 3014
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 132 Sequence Number: - Start / End Page: - Identifier: -