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  1. Home
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  3. On-Line Learning in Neural Networks
  • Cited by 61
Publisher:
Cambridge University Press
Online publication date:
January 2010
Print publication year:
1999
Online ISBN:
9780511569920
DOI:
https://doi.org/10.1017/CBO9780511569920
Subjects:
Mathematical Physics, Computer Science, Pattern Recognition and Machine Learning, Scientific Computing, Scientific Software
Series:
Publications of the Newton Institute (17)
Information

Book description

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.

Reviews

Review of the hardback:‘I recommend this book to readers with a theoretical, analytical, or mathematical interest in neural networks, especially online learning.’

Source: Computing Reviews

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