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  A kernel approach for learning from almost orthogonal patterns

Schölkopf, B., Weston, J., Eskin, E., Leslie, C., & Noble, W. (2002). A kernel approach for learning from almost orthogonal patterns. In T. Elomaa, H. Mannila, & H. Toivonen (Eds.), Machine Learning: ECML 2002: 13th European Conference on Machine Learning Helsinki, Finland, August 19–23, 2002 (pp. 511-528). Berlin, Germany: Springer.

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 Creators:
Schölkopf, B1, 2, Author           
Weston, J1, 2, Author           
Eskin , E, Author
Leslie, C, Author
Noble, WS, Author
Affiliations:
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: In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well. We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine.

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 Dates: 2002-08
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 1770
DOI: 10.1007/3-540-36755-1_44
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Title: 13th European Conference on Machine Learning (ECML 2002)
Place of Event: Helsinki, Finland
Start-/End Date: 2002-08-19 - 2002-08-23

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Title: Machine Learning: ECML 2002: 13th European Conference on Machine Learning Helsinki, Finland, August 19–23, 2002
Source Genre: Proceedings
 Creator(s):
Elomaa, T, Editor
Mannila, H, Editor
Toivonen, H, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 511 - 528 Identifier: ISBN: 978-3-540-44036-9

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Pages: - Volume / Issue: 2430 Sequence Number: - Start / End Page: - Identifier: -