Training a Support Vector Machine in the Primal
2007
Book Chapter
ei
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 that there is no reason to ignore this possibility. On the contrary, from the primal point of view new families of algorithms for large scale SVM training can be investigated.
Author(s): | Chapelle, O. |
Book Title: | Large Scale Kernel Machines |
Pages: | 29-50 |
Year: | 2007 |
Month: | September |
Day: | 0 |
Series: | Neural Information Processing |
Editors: | Bottou, L. , O. Chapelle, D. DeCoste, J. Weston |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Book Chapter (inbook) |
Address: | Cambridge, MA, USA |
Digital: | 0 |
Language: | en |
Note: | This is a slightly updated version of the Neural Computation paper |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PDF
Web |
BibTex @inbook{4178, title = {Training a Support Vector Machine in the Primal}, author = {Chapelle, O.}, booktitle = {Large Scale Kernel Machines}, pages = {29-50}, series = {Neural Information Processing}, editors = {Bottou, L. , O. Chapelle, D. DeCoste, J. Weston}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = sep, year = {2007}, note = {This is a slightly updated version of the Neural Computation paper}, doi = {}, month_numeric = {9} } |