Class-Size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification
Abstract
We consider the problem of deriving class-size independent generaliza- tion bounds for some regularized discriminative multi-category classi- fication methods. In particular, we obtain an expected generalization bound for a standard formulation of multi-category support vector ma- chines. Based on the theoretical result, we argue that the formula- tion over-penalizes misclassification error, which in theory may lead to poor generalization performance. A remedy, based on a generalization of multi-category logistic regression (conditional maximum entropy), is then proposed, and its theoretical properties are examined.
Cite
Text
Zhang. "Class-Size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification." Neural Information Processing Systems, 2004.Markdown
[Zhang. "Class-Size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/zhang2004neurips-classsize/)BibTeX
@inproceedings{zhang2004neurips-classsize,
title = {{Class-Size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification}},
author = {Zhang, Tong},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1625-1632},
url = {https://mlanthology.org/neurips/2004/zhang2004neurips-classsize/}
}