On-Line Learning in Neural Networks: 17 (Publications of the Newton Institute, Series Number 17)

ISBN 10: 0521652634 / ISBN 13: 9780521652636

Published by Cambridge University Press, 1999

About this Item

Edited volume written by leading experts providing state-of-art survey in on-line learning and neural networks. Editor(s): Saad, David. Series: Publications of the Newton Institute. Num Pages: 412 pages, 40 b/w illus. BIC Classification: UYQN. Category: (P) Professional & Vocational. Dimension: 235 x 159 x 31. Weight in Grams: 2362. . 1999. Illustrated. hardcover. . . . .

Seller Inventory # V9780521652636

Bibliographic Details

• Title: On-Line Learning in Neural Networks: 17 (Publications of the Newton Institute, Series Number 17)
• Publisher: Cambridge University Press
• Publication Date: 1999
• Language: English
• ISBN 10: 0521652634
• ISBN 13: 9780521652636
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

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.