On-Line Learning in Neural Networks

Paperback (Print)
Buy New
Buy New from BN.com
$57.29
Used and New from Other Sellers
Used and New from Other Sellers
from $57.82
Usually ships in 1-2 business days
(Save 6%)
Other sellers (Paperback)
  • All (6) from $57.82   
  • New (3) from $57.82   
  • Used (3) from $75.14   

Overview

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 online 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.
Read More Show Less

Editorial Reviews

From the Publisher
"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

Read More Show Less

Product Details

Table of Contents

Acknowledgements
List of contributors
Foreword 1
1 Introduction 3
2 On-line Learning and Stochastic Approximations 9
3 Exact and Perturbation Solutions for the Ensemble Dynamics 43
4 A Statistical Study of On-line Learning 63
5 On-line Learning in Switching and Drifting Environments with Application to Blind Source Separation 93
6 Parameter Adaptation in Stochastic Optimization 111
7 Optimal On-line Learning in Multilayer Neural Networks 135
8 Universal Asymptotics in Committee Machines with Tree Architecture 165
9 Incorporating Curvature Information into On-line Learning 183
10 Annealed On-line Learning in Multilayer Neural Networks 209
11 On-line Learning of Prototypes and Principal Components 231
12 On-line Learning with Time-Correlated Examples 251
13 On-line Learning from Finite Training Sets 279
14 Dynamics of Supervised Learning with Restricted Training Sets 303
15 On-line Learning of a Decision Boundary with and without Queries 345
16 A Bayesian Approach to On-line Learning 363
17 Optimal Perceptron Learning: an On-line Bayesian Approach 379
Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)