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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 461838.0, MPI für biologische Kybernetik / Biologische Kybernetik
An introduction to kernel learning algorithms
Authors:Gehler, P.V.; Schölkopf, B.
Editors:Camps-Valls, G.; Bruzzone, L.
Place of Publication:New York, NY, USA
Publisher:Wiley
Date of Publication (YYYY-MM-DD):2009-10
Title of Book:Kernel Methods for Remote Sensing Data Analysis
Start Page:25
End Page:48
Physical Description:24
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
Comment of the Author/Creator:ISBN: 978-0-470-72211-4
External Publication Status:published
Document Type:InBook
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6120
URL:http://eu.wiley.com/WileyCDA/WileyTitle/productCd-...
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