PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An introduction to kernel learning algorithms
P.V. Gehler and B. Schölkopf
In: Kernel Methods for Remote Sensing Data Analysis (2009) Wiley , New York, NY, USA , pp. 25-48. ISBN 978-0-470-72211-4


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.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:6326
Deposited By:Bernhard Schölkopf
Deposited On:08 March 2010