Understanding Stepwise Generalization of Support Vector Machines: A Toy Model
Abstract
In this article we study the effects of introducing structure in the input distribution of the data to be learnt by a simple perceptron. We determine the learning curves within the framework of Statis(cid:173) tical Mechanics. Stepwise generalization occurs as a function of the number of examples when the distribution of patterns is highly anisotropic. Although extremely simple, the model seems to cap(cid:173) ture the relevant features of a class of Support Vector Machines which was recently shown to present this behavior.
Cite
Text
Risau-Gusman and Gordon. "Understanding Stepwise Generalization of Support Vector Machines: A Toy Model." Neural Information Processing Systems, 1999.Markdown
[Risau-Gusman and Gordon. "Understanding Stepwise Generalization of Support Vector Machines: A Toy Model." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/risaugusman1999neurips-understanding/)BibTeX
@inproceedings{risaugusman1999neurips-understanding,
title = {{Understanding Stepwise Generalization of Support Vector Machines: A Toy Model}},
author = {Risau-Gusman, Sebastian and Gordon, Mirta B.},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {321-327},
url = {https://mlanthology.org/neurips/1999/risaugusman1999neurips-understanding/}
}