Date of Original Version
Abstract or Description
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 structure induced by the Fisher information. This leads to a new view of hyperplane classifiers which, together with a generalized margin concept, shows how to adapt existing margin-based hyperplane models to multinomial geometry. Experiments show the new classification framework to be effective for text classification, where the categorical structure of the data is modeled naturally within the multinomial family.
Proceedings of the Twenty-First international Conference on Machine Learning, ICML '04, 69, 66.