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A hilbert space embedding for distributions

Smola, A; Gretton, A; Song, L; Schölkopf, B; (2007) A hilbert space embedding for distributions. In: (pp. pp. 13-31).

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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 Hubert 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. © Springer-Verlag Berlin Heidelberg 2007.

Type: Proceedings paper
Title: A hilbert space embedding for distributions
ISBN-13: 9783540752240
URI: http://discovery.ucl.ac.uk/id/eprint/1334327
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