This paper studies a new generative supervised classifier which assumes that the parameter prior distributions conditioned on each class are mixtures of ...

Feb 4, 2022 · Abstract—Choosing the appropriate parameter prior distributions associated to a given Bayesian model is a challenging problem.

This paper studies a new generative supervised classifier which assumes that the parameter prior distributions conditioned on each class are mixtures of ...

This paper studies a new generative supervised classifier which assumes that the parameter prior distributions conditioned on each class are mixtures of ...

Generative Supervised Classification Using Dirichlet Process Priors. Choosing the appropriate parameter prior distributions associated to a given Bayesian ...

Abstract—We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution.

A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers.

Abstract. We develop a Bayesian framework for tackling the supervised clustering problem, the generic prob- lem encountered in tasks such as reference ...

Abstract. In this paper we propose a matrix-variate Dirichlet process (MATDP) for modeling the joint prior of a set of random matrices. Our approach is able.

A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers.