PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Hilbert Space Embedding for Distributions
Alex Smola, Arthur Gretton, Le Song and Bernhard Schölkopf
In: Algorithmic Learning Theory Lecture Notes in Computer Science , 4754 . (2007) Springer Berlin/Hidelberg , Sendai, Japan , pp. 13-31. ISBN 978-3-540-75224-0

Abstract

We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3987
Deposited By:Alex Smola
Deposited On:25 February 2008