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  Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten

Schölkopf, B., Müller, K.-R., & Smola, A. (1999). Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Informatik - Forschung und Entwicklung, 14(3), 154-163. doi:10.1007/s004500050135.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E657-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-BF8B-8
Genre: Journal Article

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 Creators:
Schölkopf, B1, Author              
Müller, K-R, Author              
Smola, AJ, Author              
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1External Organizations, ou_persistent22              

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 Abstract: We describe recent developments and results of statistical learning theory. In the framework of learning from examples, two factors control generalization ability: explaining the training data by a learning machine of a suitable complexity. We describe kernel algorithms in feature spaces as elegant and efficient methods of realizing such machines. Examples thereof are Support Vector Machines (SVM) and Kernel PCA (Principal Component Analysis). More important than any individual example of a kernel algorithm, however, is the insight that any algorithm that can be cast in terms of dot products can be generalized to a nonlinear setting using kernels. Finally, we illustrate the significance of kernel algorithms by briefly describing industrial and academic applications, including ones where we obtained benchmark record results.

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 Dates: 1999-09
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1007/s004500050135
BibTex Citekey: 733
 Degree: -

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Title: Informatik - Forschung und Entwicklung
Source Genre: Journal
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Publ. Info: Berlin : Springer-Verlag
Pages: - Volume / Issue: 14 (3) Sequence Number: - Start / End Page: 154 - 163 Identifier: ISSN: 0178-3564
CoNE: https://pure.mpg.de/cone/journals/resource/954933107333