On-Line Learning in Neural Networks

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On-line learning is one of the most commonly used techniques for training neural networks. Though it has been used successfully in many real-world applications, most training methods are based on heuristic observations. The lack of theoretical support damages the credibility as well as the efficiency of neural networks training, making it hard to choose reliable or optimal methods. This book presents a coherent picture of the state of the art in the theoretical analysis of online 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 nonexperts 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, both in industry and academia.
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Editorial Reviews

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

"The introduction gives a nice overview of on-line learning in neural networks and relates the subject to other developments in neural networks. The material provides a comprehensive view of the subject and is accessible to mathematicians, statisticians, and engineers in both industry and academia." Journal of the American Statistical Association

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Product Details

Table of Contents

List of contributors
Foreword 1
1 Introduction 3
2 On-line Learning and Stochastic Approximations 9
3 Exact and Perturbation Solutions for the Ensemble Dynamics 43
4 A Statistical Study of On-line Learning 63
5 On-line Learning in Switching and Drifting Environments with Application to Blind Source Separation 93
6 Parameter Adaptation in Stochastic Optimization 111
7 Optimal On-line Learning in Multilayer Neural Networks 135
8 Universal Asymptotics in Committee Machines with Tree Architecture 165
9 Incorporating Curvature Information into On-line Learning 183
10 Annealed On-line Learning in Multilayer Neural Networks 209
11 On-line Learning of Prototypes and Principal Components 231
12 On-line Learning with Time-Correlated Examples 251
13 On-line Learning from Finite Training Sets 279
14 Dynamics of Supervised Learning with Restricted Training Sets 303
15 On-line Learning of a Decision Boundary with and without Queries 345
16 A Bayesian Approach to On-line Learning 363
17 Optimal Perceptron Learning: an On-line Bayesian Approach 379
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