PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Discriminative Joint Density Modeling
Jarkko Salojärvi, Kai Puolamäki and Samuel Kaski
In: ECML 2005, 3-7 Oct 2005, Porto, Portugal.

Abstract

We study discriminative joint density models, that is, generative models for the joint density p(c,x) learned by maximizing a discriminative cost function, the conditional likelihood. We use the framework to derive generative models for generalized linear models, including logistic regression, linear discriminant analysis, and discriminative mixture of unigrams. The benefits of deriving the discriminative models from joint density models are that it is easy to extend the models and interpret the results, and missing data can be treated using justified standard methods.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:http://www.cis.hut.fi/projects/mi/papers/ecml05.pdf
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1087
Deposited By:Jarkko Salojärvi
Deposited On:18 September 2005