Hyperplane Margin Classifiers on the Multinomial Manifold
Abstract
The assumptions behind linear classifiers for categorical data are examined and reformulated in the context of the multinomial manifold, the simplex of multinomial models furnished with the Riemannian structureinduced by the Fisher information. This leads to a new view of hyperplaneclassifiers which, together with a generalized margin concept, shows how toadapt existing margin-based hyperplane models to multinomial geometry. Experiments show the new classification framework to be effective for textclassification, where the categorical structure of the data is modelednaturally within the multinomial family.
Cite
Text
Lebanon and Lafferty. "Hyperplane Margin Classifiers on the Multinomial Manifold." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015333Markdown
[Lebanon and Lafferty. "Hyperplane Margin Classifiers on the Multinomial Manifold." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/lebanon2004icml-hyperplane/) doi:10.1145/1015330.1015333BibTeX
@inproceedings{lebanon2004icml-hyperplane,
title = {{Hyperplane Margin Classifiers on the Multinomial Manifold}},
author = {Lebanon, Guy and Lafferty, John D.},
booktitle = {International Conference on Machine Learning},
year = {2004},
doi = {10.1145/1015330.1015333},
url = {https://mlanthology.org/icml/2004/lebanon2004icml-hyperplane/}
}