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On-Line Learning in Neural Networks (Publications of the Newton Institute)
 
 
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On-Line Learning in Neural Networks (Publications of the Newton Institute) [Hardcover]

David Saad (Editor)

Price: $142.00 & this item ships for FREE with Super Saver Shipping. Details
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Book Description

0521652634 978-0521652636 February 13, 1999 1st
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 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 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

Review

"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

Book Description

On-line learning is one of the most commonly used techniques for training neural networks, and has been used successfully in many real-world applications. The aim of this book is to present a coherent picture of the state-of-the-art in on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. There follow surveys by leading experts in the field that combine new and established material and enable non-experts to learn more about the techniques and methods used.

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Inside This Book (learn more)
First Sentence:
The convergence of online learning algorithms is analyzed using the tools of the stochastic approximation theory, and proved under very weak conditions. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
isotropic teacher, dynamical replica theory, restricted training sets, locally optimal rule, online gradient descent, randomized learning, optimal learning rate, batch gradient descent algorithm, small learning parameters, corresponding generalization error, teacher weight vectors, symmetric plateau, infinite training sets, student vectors, natural gradient learning, teacher nodes, continuous gradient descent, soft committee machine, greedy phase, optimal asymptotic performance, offline learning, student activations, using curvature information, online learning algorithms, matrix momentum
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Neural Information Processing Systems, New York, Neural Computation, San Mateo, Morgan Kaufmann, References Amari, Annealed Online Learning, Optimal Perceptron Learning, Springer Verlag, World Scientific, Oxford University Press, On--line Learning, Technical Report, Academic Press, Aston University Birmingham, Biological Cybernetics, Introduction Neural, The Netherlands, The Statistical Mechanics Perspective, University of Nijmegen
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