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  Classification in a Normalized Feature Space using Support Vector Machines

Graf, A., Smola, A., & Borer, S. (2003). Classification in a Normalized Feature Space using Support Vector Machines. IEEE Transactions on Neural Networks, 14(3), 597-605. doi:10.1109/TNN.2003.811708.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DC7D-C Version Permalink: http://hdl.handle.net/21.11116/0000-0005-6A9B-6
Genre: Journal Article

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 Creators:
Graf, ABA1, 2, 3, Author              
Smola, AJ, Author              
Borer, S, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: This paper discusses classification using support vector machines in a normalized feature space. We consider both normalization in input space and in feature space. Exploiting the fact that in this setting all points lie on the surface of a unit hypersphere we replace the optimal separating hyperplane by one that is symmetric in its angles, leading to an improved estimator. Evaluation of these considerations is done in numerical experiments on two real-world datasets. The stability to noise of this offset correction is subsequently investigated as well as its optimality.

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 Dates: 2003-05
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1109/TNN.2003.811708
BibTex Citekey: 2102
 Degree: -

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Title: IEEE Transactions on Neural Networks
Source Genre: Journal
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Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 14 (3) Sequence Number: - Start / End Page: 597 - 605 Identifier: ISSN: 1045-9227
CoNE: https://pure.mpg.de/cone/journals/resource/954925591430