- Paperback: 404 pages
- Publisher: Cambridge University Press; 1 edition (August 20, 2009)
- Language: English
- ISBN-10: 052111862X
- ISBN-13: 978-0521118620
- Product Dimensions: 6 x 0.9 x 9 inches
- Shipping Weight: 1.5 pounds (View shipping rates and policies)
- Average Customer Review: Be the first to review this item
- Amazon Best Sellers Rank: #410,108 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Neural Network Learning: Theoretical Foundations 1st Edition
Use the Amazon App to scan ISBNs and compare prices.
All Books, All the Time
Read author interviews, book reviews, editors picks, and more at the Amazon Book Review. Read it now
Frequently bought together
Customers who bought this item also bought
"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
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.