What Is Bayesian Learning In Machine Learning
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Bayesian Reasoning and Machine Learning
Author: David Barber
language: en
Publisher: Cambridge University Press
Release Date: 2012-02-02
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Advanced Lectures on Machine Learning
Author: Olivier Bousquet
language: en
Publisher: Springer Science & Business Media
Release Date: 2004-09-02
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.
Bayesian Learning for Neural Networks
Author: Radford M. Neal
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
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.