Empirical Inference
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.
| Author(s): | Smola, A. and Gretton, A. and Song, L. and Schölkopf, B. |
| Links: | |
| Book Title: | Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 |
| Journal: | Algorithmic Learning Theory: 18th International Conference (ALT 2007) |
| Pages: | 13-31 |
| Year: | 2007 |
| Month: | October |
| Day: | 0 |
| Editors: | M Hutter and RA Servedio and E Takimoto |
| Publisher: | Springer |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| DOI: | 10.1007/978-3-540-75225-7_5 |
| Event Name: | 18th International Conference on Algorithmic Learning Theory (ALT 2007) |
| Event Place: | Sendai, Japan |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{4645,
title = {A Hilbert Space Embedding for Distributions},
journal = {Algorithmic Learning Theory: 18th International Conference (ALT 2007)},
booktitle = {Algorithmic Learning Theory, Lecture Notes in Computer Science 4754 },
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.},
pages = {13-31},
editors = {M Hutter and RA Servedio and E Takimoto},
publisher = {Springer},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Berlin, Germany},
month = oct,
year = {2007},
author = {Smola, A. and Gretton, A. and Song, L. and Sch{\"o}lkopf, B.},
doi = {10.1007/978-3-540-75225-7_5},
month_numeric = {10}
}