Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.
| Author(s): | Schölkopf, B. |
| Journal: | Jahrbuch der Max-Planck-Gesellschaft |
| Volume: | 2004 |
| Pages: | 377-382 |
| Year: | 2004 |
| Day: | 0 |
| Bibtex Type: | Miscellaneous (misc) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | de |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@misc{2811,
title = {Statistische Lerntheorie und Empirische Inferenz},
journal = {Jahrbuch der Max-Planck-Gesellschaft},
abstract = {Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.},
volume = {2004},
pages = {377-382},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
year = {2004},
slug = {2811},
author = {Sch{\"o}lkopf, B.}
}