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  Incorporating Invariances in Non-Linear Support Vector Machines

Chapelle, O., & Schölkopf, B.(2001). Incorporating Invariances in Non-Linear Support Vector Machines.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E3A0-3 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E3A1-1
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 Creators:
Chapelle, O1, Author              
Schölkopf, B1, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We consider the problem of how to incorporate in the Support Vector Machine (SVM ) framework invariances given by some a priori known transformations under which t he data should be invariant. It extends some previous work which was only applicab le with linear SVMs and we show on a digit recognition task that the proposed appro ach is superior to the traditional Virtual Support Vector method.

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 Dates: 2001
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2166
 Degree: -

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