A Variational Bayesian Committee of Neural Networks (1999)

Cached

Download Links

by Neil D. Lawrence , Mehdi Azzouzi
Citations:5 - 3 self

Active Bibliography

23 Ensemble learning in Bayesian neural networks – David Barber, Christopher M. Bishop - 1998
18 Bayesian Approach for Neural Networks - Review and Case Studies – Jouko Lampinen, Aki Vehtari - 2001
11 Building Blocks For Variational Bayesian Learning Of Latent Variable Models – Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen - 2006
87 An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models – Harri Valpola, Juha Karhunen - 2001
Variational Cumulant Expansions for Intractable Distributions – PiĆ«rre van de Laar, David Barber - 1999
2 Variational Cumulant Expansions for Intractable Distributions – David Barber, Pierre van de Laar - 1999
67 Comparison of Approximate Methods for Handling Hyperparameters – David J.C. MacKay
35 Classification and Regression using Mixtures of Experts – Steven Richard Waterhouse - 1997
University of Cambridge. – unknown authors
4 Variational learning in graphical models and neural networks – Christopher M. Bishop - 1998
45 Ensemble Learning For Independent Component Analysis – Harri Lappalainen - 1999
7 Mixture Representations for Inference and Learning in Boltzmann Machines – Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan - 1998
1 Markovian Inference in Belief Networks – Brendan Frey, Neil D. Lawrence, Christopher M. Bishop - 1998
3 Modelling Conditional Probability Densities with Neural Networks – Dirk Husmeier - 1997
Oxford University. – unknown authors - 2000
1 Divide and Conquer: Pattern Recognition using Mixtures of Experts – Steve Waterhouse, Steve Waterhouse - 1997
Implementation of Gaussian Process models for . . . – Keith Russell Thompson - 2009
18 Ensemble Learning and Evidence Maximization – David J.C. MacKay, F \gamma - 1995
Nonlinear Dynamical Factor Analysis – Xavier Giannakopoulos, Harri Valpola