Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with...
We present an approach to semi-supervised learning based on an exponential family characterization. Our approach generalizes previous work on coupled priors for hybrid generative/...
In this paper we propose a novel classification algorithm that fits models of different complexity on separate regions of the input space. The goal is to achieve a balance betwee...
Ricardo Vilalta, Murali-Krishna Achari, Christoph ...
We introduce quadratically gated mixture of experts (QGME), a statistical model for multi-class nonlinear classification. The QGME is formulated in the setting of incomplete data,...
We show a close relationship between the Expectation - Maximization (EM) algorithm and direct optimization algorithms such as gradientbased methods for parameter learning. We iden...
Ruslan Salakhutdinov, Sam T. Roweis, Zoubin Ghahra...