Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks

Author: Radford M. Neal

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 194

ISBN-13: 1461207452

DOWNLOAD EBOOK

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.

Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks

Author: Radford M. Neal

Publisher: Springer

Published: 1996-09-04

Total Pages: 204

ISBN-13: 0387947248

DOWNLOAD EBOOK

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.

Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks

Author: Radford M. Neal

Publisher: Springer

Published: 1996-08-09

Total Pages: 0

ISBN-13: 9780387947242

DOWNLOAD EBOOK

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.

Learning Bayesian Networks
Learning Bayesian Networks

Author: Richard E. Neapolitan

Publisher: Prentice Hall

Published: 2004

Total Pages: 704

ISBN-13:

DOWNLOAD EBOOK

In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.

Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning

Author: David Barber

Publisher: Cambridge University Press

Published: 2012-02-02

Total Pages: 739

ISBN-13: 0521518148

DOWNLOAD EBOOK

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Bayesian Nonparametrics via Neural Networks
Bayesian Nonparametrics via Neural Networks

Author: Herbert K. H. Lee

Publisher: SIAM

Published: 2004-01-01

Total Pages: 106

ISBN-13: 9780898718423

DOWNLOAD EBOOK

Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

Advanced Lectures on Machine Learning
Advanced Lectures on Machine Learning

Author: Olivier Bousquet

Publisher: Springer

Published: 2011-03-22

Total Pages: 249

ISBN-13: 3540286500

DOWNLOAD EBOOK

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Variational Bayesian Learning Theory
Variational Bayesian Learning Theory

Author: Shinichi Nakajima

Publisher: Cambridge University Press

Published: 2019-07-11

Total Pages: 561

ISBN-13: 1107076153

DOWNLOAD EBOOK

This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.