Bayesian Learning for Neural Networks [electronic resource] /

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

Bibliographic Details

Main Authors:	Neal, Radford M. author., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	New York, NY : Springer New York : Imprint: Springer, 1996
Subjects:	Mathematics., Artificial intelligence., Computer simulation., Probabilities., Statistics., Probability Theory and Stochastic Processes., Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences., Artificial Intelligence (incl. Robotics)., Simulation and Modeling.,
Online Access:	http://dx.doi.org/10.1007/978-1-4612-0745-0
Tags:	Add Tag No Tags, Be the first to tag this record!