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  An Introduction to Kernel-Based Learning Algorithms

Müller, K., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2002). An Introduction to Kernel-Based Learning Algorithms. In Y. Hu, J.-N. Hwang, & S. Perry (Eds.), Handbook of neural network signal processing: Neural network signal processing (pp. 95-134). Boca Raton, FL, USA: CRC Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E5D3-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-8E05-6
Genre: Book Chapter

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https://dl.acm.org/doi/book/10.5555/557884 (Table of contents)
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 Creators:
Müller, KR, Author              
Mika, S, Author
Rätsch, G, Author              
Tsuda, K1, Author              
Schölkopf, B1, 2, 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, ou_1497794              

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 Abstract: This chapter provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis as examples for successful kernel-based learning methods. It presents a short background about Vapnik–Chervonenkis theory and kernel feature spaces and then proceeds to kernel-based learning in supervised and unsupervised scenarios, including practical and algorithmic considerations. The chapter illustrates the usefulness of kernel algorithms by finally discussing applications such as optical character recognition and DNA analysis. The kernel-Adatron is derived from the Adatron algorithm originally proposed by Anlauf and Biehl in a statistical mechanics setting. The kernel-Adatron constructs a large margin hyperplane using online learning. Its implementation is very simple. The chapter describes selected interesting applications of supervised and unsupervised learning with kernels. It demonstrates that kernel-based approaches achieve competitive results over a whole range of benchmarks with different noise levels and robustness requirements.

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 Dates: 2002
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 1845
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Source 1

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Title: Handbook of neural network signal processing: Neural network signal processing
Source Genre: Book
 Creator(s):
Hu, YH, Editor
Hwang, J-N, Editor
Perry, SW, Editor
Affiliations:
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Publ. Info: Boca Raton, FL, USA : CRC Press
Pages: 408 Volume / Issue: - Sequence Number: 4 Start / End Page: 95 - 134 Identifier: ISBN: 0-8493-2359-2

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Title: The electrical engineering and applied signal processing series
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -