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Neural Network Learning: Theoretical Foundations Hardcover – November 13, 1999

ISBN-13: 978-0521573535 ISBN-10: 052157353X Edition: 1st

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Editorial Reviews


"This book gives a thorough but nevertheless self-contained treatment of neural network learning from the perspective of computational learning theory." Mathematical Reviews

"This book is a rigorous treatise on neural networks that is written for advanced graduate students in computer science. Each chapter has a bibliographical section with helpful suggestions for further reading...this book would be best utilized within an advanced seminar context where the student would be assisted with examples, exercises, and elaborative comments provided by the professor." Telegraphic Reviews

Book Description

This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. The authors also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is essentially self-contained, since it introduces the necessary background material on probability, statistics, combinatorics and computational complexity; and it is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.

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Product Details

  • Hardcover: 403 pages
  • Publisher: Cambridge University Press; 1 edition (November 13, 1999)
  • Language: English
  • ISBN-10: 052157353X
  • ISBN-13: 978-0521573535
  • Product Dimensions: 6 x 1 x 9 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #3,995,677 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

24 of 82 people found the following review helpful By A Customer on June 28, 1999
Format: Hardcover
A stonking blockbuster of a book, filled with raw power and suspense! From the heart stopping narrative on the on the Need for Conditions on the Activation Functions, to the torrid account of Classes of Finite Pseudo-dimension, this is truly the most exhilarating and disturbing description yet of the mathematical foundations of machine learning. I eagerly await the sequel.
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Neural Network Learning: Theoretical Foundations
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