On-Line Learning in Neural Networks

By: David Saad (Editor)

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Hardcover | 29 March 1999

At a Glance

Hardcover
412 Pages

Dimensions(cm)
22.86 x 15.24 x 2.69

Hardcover

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On-line learning is one of the most powerful and commonly used techniques for training large layered networks, and has been used successfully in many real-world applications. Traditional analytical methods have been recently complemented by ones from statistical physics and Bayesian statistics. This powerful combination of analytical methods provides more insight and deeper understanding of existing algorithms, and leads to novel and principled proposals for their improvement. This book presents a coherent picture of the state-of-the-art in the theoretical analysis of on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. Surveys by leading experts in the field combine new and established material and enable non-experts to learn more about the techniques and methods used. This book, the first in the area, provides a comprehensive view of the subject and will be welcomed by mathematicians, scientists and engineers, whether in industry or academia.

Industry Reviews

'I recommend this book to readers with a theoretical, analytical, or mathematical interest in neural networks, especially online learning.' Computing Reviews

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Networking & Communications Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Computer Vision

Foreword
Introduction
On-line learning and stochastic approximations
Exact and perturbative solutions for the ensemble dynamics
A statistical study of on-line learning
On-line learning in switching and drifting environments
Parameter adaptation in stochastic optimization
Optimal on-line learning for multilayer neural networks
Universal asymptotics in committee machines with tree architecture
Incorporating curvature information in on-line learning
Annealed on-line learning in multilayer networks
On-line learning of prototypes and principal components
On-line learning with time-correlated patterns Tom Heskes and Wim Wiegerinck
On-line learning from finite training sets
Dynamics of supervised learning with restricted training sets
On-line learning of a decision boundary with and without queries
A Bayesian approach to on-line learning
Optimal perceptron learning: an on-line Bayesian approach
Table of Contents provided by Publisher. All Rights Reserved.

On-Line Learning in Neural Networks

At a Glance

Hardcover

Industry Reviews

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How to return your order

Defective items

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