The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. This framework can be applied to feedforward networks, making possible (1) objective comparisons between solutions using alternative network architectures; (2) objective choice of magnitude and type of weight decay terms; (3) quantified estimates of the error bars on network parameters and on network output. The framework also generates a measure of the effective number of parameters determined by the data. The relationship of Bayesian model comparison to recent work on prediction of generalisation ability (Guyon et al., 1992, Moody, 1992) is discussed
PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construc...
© 2005 Modelling & Simulation Society of Australia & New ZealandArtificial neural networks (ANNs) ha...
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural ne...
The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. Th...
mackayGras.phy.cam.ac.uk The Bayesian model comparison framework is reviewed, and the Bayesian Occam...
A quantitative and practical Bayesian framework is described for learning of mappings in feedforward...
A quantitative and practical Bayesian framework is described for learn-ing of mappings in feedforwar...
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
thodberg~nn.meatre.dk MacKay's Bayesian framework for backpropagation is conceptually appealing...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison ha...
This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probabilit...
The purpose of this thesis is to compare different classification methods, on the basis of the resul...
A Bayesian network (BN) is a probabilistic graphical model with applications in knowledge discovery ...
PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construc...
© 2005 Modelling & Simulation Society of Australia & New ZealandArtificial neural networks (ANNs) ha...
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural ne...
The Bayesian model comparison framework is reviewed, and the Bayesian Occam's razor is explained. Th...
mackayGras.phy.cam.ac.uk The Bayesian model comparison framework is reviewed, and the Bayesian Occam...
A quantitative and practical Bayesian framework is described for learning of mappings in feedforward...
A quantitative and practical Bayesian framework is described for learn-ing of mappings in feedforwar...
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
thodberg~nn.meatre.dk MacKay's Bayesian framework for backpropagation is conceptually appealing...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison ha...
This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probabilit...
The purpose of this thesis is to compare different classification methods, on the basis of the resul...
A Bayesian network (BN) is a probabilistic graphical model with applications in knowledge discovery ...
PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construc...
© 2005 Modelling & Simulation Society of Australia & New ZealandArtificial neural networks (ANNs) ha...
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural ne...