Adaptive Learning of Polynomial Networks Genetic Programming, Backpr
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contrib
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GENETIC AND EVOLUTIONARY COMPUTATION SERIES Series Editors David E. Goldberg University of Illinois at Urbana-Champaign John R. Koza Stanford University Selected titles from the Series: THE DESIGN OF INNOVATION: Lessons from and for Competent Genetic Algorithms, David E. Goldberg; ISBN: 1-4020-7098-5 GENETIC PROGRAMMING IV: Routine Human-Computer Machine Intelligence, John R. Koza, Martin A. Keane, Matthew J. Streeter, William Mydlowec, lessen Yu, Guido Lanza; ISBN: 1-4020-7446-8; softcover ISBN: 0-387-25067-0 EVOLUTIONARY ALGORITHMS FOR SOLVING MULTIOBJECTIVE PROBLEMS, Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont; ISBN: 0-306-46762-3 AUTOMATIC QUANTUM COMPUTER PROGRAMMING: A Genetic Programming Approach, Lee Spector, ISBN: 1-4020-7894-3 GENETIC PROGRAMMING AND DATA STRUCTURES: Genetic Programming + Data Structures = Automatic Programming! William B. Langdon; ISBN: 0-7923-8135-1 For a complete listing of books in this series, go to http://www.springer.com
Adaptive Learning of Polynomial Networks Genetic Programming, Backpropagation and Bayesian Methods
Nikolay Y. Nikolaev University of London
Hitoshi Iba The University of Tokyo
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Nikolay Y. Nikolaev University of London Hitoshi Iba The University of Tokyo Library of Congress Control Number: 2006920797 ISBN-10: 0-387-31239-0 ISBN-13: 978-0387-312392
e-ISBN-10: 0-387-31240-4 e-ISBN-13: 978-0387-31240-
© 2006 by Springer Science+Business Media, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science -f- Business Media, Inc., 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America
987654321 springer.com
Contents
Preface
xi
1. INTRODUCTION 1.1 Inductive Learning 1.1.1 Learning and Regression 1.1.2 Polynomial Models 1.1.3 Inductive Computation Machinery 1.2 Why Polynomial Networks? 1.2.1 Advantages of Polynomial Networks 1.2.2 Multilayer Polynomial Networks
1 3 4 5 5 7 8 9
1.3 1.4 1.5 1.6 1.7
Evolutionary Search 1.3.1 STROGANOFF and its Variants Neural Network Training Bayesian Inference Statistical Model Vahdation Organization of the Book
2. INDUCTIVE GENETIC PROGRAMMING 2.1 Polynomial Neural Networks (PNN) 2.1.1 PNN Approaches 2.1.2 Tree-structured PNN 2.2 IGP Search Mechanisms 2.2.1 Sampling and Control Issues 2.2.2 Biological Interpretation 2.3 Genetic Learning Operators 2.3.1 Context-preserving Mutation 2.3.2 Crossover Operator
16 17 21 22 23 23 25 26 27 29 35 36 36 38 38 40
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