358 citations · 498 across the 3 of their papers we have counts for
3 papers
Mixture Representations for Inference and Learning in Boltzmann Machines
Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan
Boltzmann machines are undirected graphical models with two-state stochastic variables, in which the logarithms of the clique potentials are quadratic functions of the node states.…
Variational Relevance Vector Machines
Christopher M. Bishop, Michael Tipping
The Support Vector Machine (SVM) of Vapnik (1998) has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictio…
Bayesian Hierarchical Mixtures of Experts
Christopher M. Bishop, Markus Svensen
The Hierarchical Mixture of Experts (HME) is a well-known tree-based model for regression and classification, based on soft probabilistic splits. In its original formulation it was…