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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Salojärvi, J., Puolamäki, K., & Kaski, S. (2005). On discriminative joint density modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3720 LNAI, pp. 341–352). Springer Verlag. https://doi.org/10.1007/11564096_34
Mendeley helps you to discover research relevant for your work.