PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Methods in Machine Learning
T. Hofmann, B. Schölkopf and A.J. Smola
Annals of Statistics Volume 36, Number 3, pp. 1171-1220, 2008.

Abstract

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:4326
Deposited By:Bernhard Schölkopf
Deposited On:13 March 2009