Markov Property in Generative Classifiers

Gherardo Varando; Concha Bielza; Pedro Larrañaga; Eva Riccomagno

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arXiv · 1811.04759

Markov Property in Generative Classifiers

Gherardo Varando·Concha Bielza·Pedro Larrañaga·Eva Riccomagno

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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.

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Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno. 2018-11-12. Markov Property in Generative Classifiers. https://arxiv.org/abs/1811.04759

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