Discriminative Mixture Models
نویسندگان
چکیده
We consider the problem of learning density mixture models for Classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent the density at all points in the sample space. Discriminative learning, on the other hand, aims at representing the density at the decision boundary. We introduce novel discriminative learning methods for mixtures of generative models.
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