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  An introduction to kernel learning algorithms

Gehler, P., & Schölkopf, B. (2009). An introduction to kernel learning algorithms. In G. Camps-Valls, & L. Bruzzone (Eds.), Kernel Methods for Remote Sensing Data Analysis (pp. 25-48). New York, NY, USA: Wiley.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C28E-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-0998-8
Genre: Book Chapter

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 Creators:
Gehler, PV1, 2, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Kernel learning algorithms are currently becoming a standard tool in the area of machine learning and pattern recognition. In this chapter we review the fundamental theory of kernel learning. As the basic building block we introduce the kernel function, which provides an elegant and general way to compare possibly very complex objects. We then review the concept of a reproducing kernel Hilbert space and state the representer theorem. Finally we give an overview of the most prominent algorithms, which are support vector classification and regression, Gaussian Processes and kernel principal analysis. With multiple kernel learning and structured output prediction we also introduce some more recent advancements in the field.

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 Dates: 2009
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1002/9780470748992.ch2
BibTex Citekey: 6120
 Degree: -

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Title: Kernel Methods for Remote Sensing Data Analysis
Source Genre: Book
 Creator(s):
Camps-Valls, G, Editor
Bruzzone, L, Editor
Affiliations:
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Publ. Info: New York, NY, USA : Wiley
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 25 - 48 Identifier: ISBN: 978-0-470-72211-4