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An introduction to kernel learning algorithms

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44483

Gehler,  PV
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C28E-C
Zusammenfassung
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.