Model Selection for Support Vector Machines
Abstract
New functionals for parameter (model) selection of Support Vector Ma(cid:173) chines are introduced based on the concepts of the span of support vec(cid:173) tors and rescaling of the feature space. It is shown that using these func(cid:173) tionals, one can both predict the best choice of parameters of the model and the relative quality of performance for any value of parameter.
Cite
Text
Chapelle and Vapnik. "Model Selection for Support Vector Machines." Neural Information Processing Systems, 1999.Markdown
[Chapelle and Vapnik. "Model Selection for Support Vector Machines." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/chapelle1999neurips-model/)BibTeX
@inproceedings{chapelle1999neurips-model,
title = {{Model Selection for Support Vector Machines}},
author = {Chapelle, Olivier and Vapnik, Vladimir},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {230-236},
url = {https://mlanthology.org/neurips/1999/chapelle1999neurips-model/}
}