Markov Property in Generative Classifiers
Machine Learning
2018-11-13 v1 Machine Learning
Abstract
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions. Discrimination functions of undirected Markov network classifiers can thus be characterized by sets of linear constraints. These constraints are represented by a second order finite difference operator over functions of categorical variables. As an application we study the expressive power of generative classifiers under the undirected Markov property and we present a general method to combine discriminative and generative classifiers.
Keywords
Cite
@article{arxiv.1811.04759,
title = {Markov Property in Generative Classifiers},
author = {Gherardo Varando and Concha Bielza and Pedro Larrañaga and Eva Riccomagno},
journal= {arXiv preprint arXiv:1811.04759},
year = {2018}
}