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Constructive Neural Networks

  • Book
  • © 2009

Overview

Editors:
  1. Leonardo Franco

    1. Dept. of Computer Science, University of Malaga , Málaga, Spain

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  2. David A. Elizondo

    1. Centre for Computational Intelligence, School of Computing, De Montfort University, The Gateway , Leicester, UK

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  3. José M. Jerez

    1. Dept. of Computer Science, University of Malaga , Málaga, Spain

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  • Presents the state of the art of Constructive Algorithms for Neural Networks

Part of the book series: Studies in Computational Intelligence (SCI, volume 258)

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Table of contents (15 chapters)

  1. Front Matter

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  2. Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks

    • Maria do Carmo Nicoletti, João R. Bertini Jr., David Elizondo, Leonardo Franco, José M. Jerez
    Pages 1-23

  3. Efficient Constructive Techniques for Training Switching Neural Networks

    • Enrico Ferrari, Marco Muselli
    Pages 25-48

  4. Constructive Neural Network Algorithms That Solve Highly Non-separable Problems

    • Marek Grochowski, Włodzisław Duch
    Pages 49-70

  5. On Constructing Threshold Networks for Pattern Classification

    • Martin Anthony
    Pages 71-82

  6. Self-Optimizing Neural Network 3

    • Adrian Horzyk
    Pages 83-101

  7. M-CLANN: Multiclass Concept Lattice-Based Artificial Neural Network

    • Engelbert Mephu Nguifo, Norbert Tsopze, Gilbert Tindo
    Pages 103-121

  8. Constructive Morphological Neural Networks: Some Theoretical Aspects and Experimental Results in Classification

    • Peter Sussner, Estevão Laureano Esmi
    Pages 123-144

  9. A Feedforward Constructive Neural Network Algorithm for Multiclass Tasks Based on Linear Separability

    • João Roberto Bertini Jr., Maria do Carmo Nicoletti
    Pages 145-169

  10. Analysis and Testing of the m-Class RDP Neural Network

    • David A. Elizondo, Juan M. Ortiz-de-Lazcano-Lobato, Ralph Birkenhead
    Pages 171-192

  11. Active Learning Using a Constructive Neural Network Algorithm

    • José L. Subirats, Leonardo Franco, Ignacio Molina, José M. Jerez
    Pages 193-206

  12. Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks

    • Robert Ollington, Peter Vamplew, John Swanson
    Pages 207-224

  13. A Constructive Neural Network for Evolving a Machine Controller in Real-Time

    • Andreas Huemer, David Elizondo, Mario Gongora
    Pages 225-242

  14. Avoiding Prototype Proliferation in Incremental Vector Quantization of Large Heterogeneous Datasets

    • Héctor F. Satizábal, Andres Pérez-Uribe, Marco Tomassini
    Pages 243-260

  15. Tuning Parameters in Fuzzy Growing Hierarchical Self-Organizing Networks

    • Miguel Arturo Barreto-Sanz, Andrés Pérez-Uribe, Carlos-Andres Peña-Reyes, Marco Tomassini
    Pages 261-279

  16. Self-Organizing Neural Grove: Efficient Multiple Classifier System with Pruned Self-Generating Neural Trees

    • Hirotaka Inoue
    Pages 281-291

  17. Back Matter

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Keywords

About this book

This book presents a collection of invited works that consider constructive methods for neural networks, taken primarily from papers presented at a special th session held during the 18 International Conference on Artificial Neural Networks (ICANN 2008) in September 2008 in Prague, Czech Republic. The book is devoted to constructive neural networks and other incremental learning algorithms that constitute an alternative to the standard method of finding a correct neural architecture by trial-and-error. These algorithms provide an incremental way of building neural networks with reduced topologies for classification problems. Furthermore, these techniques produce not only the multilayer topologies but the value of the connecting synaptic weights that are determined automatically by the constructing algorithm, avoiding the risk of becoming trapped in local minima as might occur when using gradient descent algorithms such as the popular back-propagation. In most cases the convergence of the constructing algorithms is guaranteed by the method used. Constructive methods for building neural networks can potentially create more compact and robust models which are easily implemented in hardware and used for embedded systems. Thus a growing amount of current research in neural networks is oriented towards this important topic. The purpose of this book is to gather together some of the leading investigators and research groups in this growing area, and to provide an overview of the most recent advances in the techniques being developed for constructive neural networks and their applications.

Editors and Affiliations

  • Dept. of Computer Science, University of Malaga , Málaga, Spain

    Leonardo Franco, José M. Jerez

  • Centre for Computational Intelligence, School of Computing, De Montfort University, The Gateway , Leicester, UK

    David A. Elizondo

Bibliographic Information

  • Book Title: Constructive Neural Networks

  • Editors: Leonardo Franco, David A. Elizondo, José M. Jerez

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-04512-7

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2009

  • Hardcover ISBN: 978-3-642-04511-0Published: 27 October 2009

  • Softcover ISBN: 978-3-642-26108-4Published: 14 March 2012

  • eBook ISBN: 978-3-642-04512-7Published: 25 November 2009

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: VIII, 293

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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