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Kernel Algorithms Distribution Embeddings in Reproducing Kernel Hilbert Spaces
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gretton, Arthur Borgwardt, Karsten M. Fukumizu, Kenji Schölkopf, Bernhard Smola, Alexander J. |
| Copyright Year | 2009 |
| Abstract | The “kernel trick” is well established as a means of constructing nonlinear algorithms from linear ones, by transferring the linear algorithms to a high dimensional feature space: specifically, a reproducing kernel Hilbert space (RKHS). Recently, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics, by embedding probability distributions in a suitable RKHS. These representations allow us to painlessly represent high order properties of distributions, and to compare distributions in a nonparametric setting. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.kyb.mpg.de/bs/pdf/2-AGBS-Kernel-Algorithms.pdf |
| Alternate Webpage(s) | http://www.kyb.mpg.de/de/bs/pdf/2-AGBS-Kernel-Algorithms.pdf |
| Alternate Webpage(s) | http://www.kyb.tuebingen.mpg.de/bs/pdf/2-AGBS-Kernel-Algorithms.pdf |
| Alternate Webpage(s) | http://www.kyb.tuebingen.mpg.de/de/bs/pdf/2-AGBS-Kernel-Algorithms.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
