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Conference Paper

A Hilbert Space Embedding for Distributions

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

Gretton,  A
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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Citation

Smola, A., Gretton, A., Song, L., & Schölkopf, B. (2007). A Hilbert Space Embedding for Distributions. Algorithmic Learning Theory: 18th International Conference (ALT 2007), 13-31.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CB8D-F
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.