Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and there is no reason for ignoring it. Moreover, from the primal point of view, new families of algorithms for large scale SVM training can be investigated.
| Author(s): | Chapelle, O. |
| Number (issue): | 147 |
| Year: | 2006 |
| Month: | April |
| Day: | 0 |
| Bibtex Type: | Technical Report (techreport) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Max Planck Institute for Biological Cybernetics, Tübingen |
| Language: | en |
| Note: | The version in the "Large Scale Kernel Machines" book is more up to date. |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@techreport{3597,
title = {Training a Support Vector Machine in the Primal},
abstract = {Most literature on Support Vector Machines (SVMs) concentrate on
the dual optimization problem. In this paper, we would like to point out
that the primal problem can also be solved efficiently, both for linear
and non-linear SVMs, and there is no reason for ignoring it. Moreover, from
the primal point of view, new families of algorithms for large scale SVM
training can be investigated.},
number = {147},
organization = {Max-Planck-Gesellschaft},
institution = {Max Planck Institute for Biological Cybernetics, Tübingen},
school = {Biologische Kybernetik},
month = apr,
year = {2006},
note = {The version in the "Large Scale Kernel Machines" book is more up to date.},
slug = {3597},
author = {Chapelle, O.},
month_numeric = {4}
}