We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.
| Author(s): | Chen, P-H. and Lin, C-J. and Schölkopf, B. |
| Journal: | Applied Stochastic Models in Business and Industry |
| Volume: | 21 |
| Number (issue): | 2 |
| Pages: | 111-136 |
| Year: | 2005 |
| Day: | 0 |
| Bibtex Type: | Article (article) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@article{3353,
title = {A tutorial on v-support vector machines},
journal = {Applied Stochastic Models in Business and Industry},
abstract = {We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called -SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.},
volume = {21},
number = {2},
pages = {111-136},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
year = {2005},
slug = {3353},
author = {Chen, P-H. and Lin, C-J. and Sch{\"o}lkopf, B.}
}