CONSTRUCTIVE NEURAL NETWORKS

L. Franco, D. Elizondo and Jerez, J.M. Editors

ISBN : 978-3-642-04511-0

Springer Series on Computational Intelligence, Vol. 258

Where to buy the book: [Springer] [Amazon] [Blackwell]

Description:

The book is a collection of chapters
devoted to Constructive methods for Neural networks. Most of the
chapters are
extended

versions of works presented at the special session on
constructive neural network algorithms held during the 18^{th}
International

Conference on Artificial Neural Networks (ICANN 2008), September 3-6,
2008 in Prague, Czech Republic.

The book is devoted to constructive neural networks and other
incremental learning algorithms that constitute an alternative

to
standard trial and error methods for searching adequate architectures.
It is made of 15 articles which provide an overview

of the most recent
advances on the techniques being developed for constructive neural
networks and their applications.

It will be of interest to researchers
in industry and academics and to post-graduate students interested in
the latest advances

and developments in the field of artificial neural
networks.

[CH1] Constructive Neural Network
Algorithms for Feedforward

Architectures Suitable for Classification Tasks

Maria do Carmo Nicoletti, Joao R. Bertini Jr., David Elizondo,

Leonardo Franco, Jose M. Jerez

[CH2] Efficient Constructive Techniques for Training Switching

Neural Networks

Enrico Ferrari, Marco Muselli

[CH3] Constructive Neural Network Algorithms That Solve

Highly Non-separable Problems

Marek Grochowski, Wlodislaw Duch

[CH4] On Constructing Threshold Networks for Pattern

Classification.

Martin Anthony

[CH5] Self-Optimizing Neural Network 3

Adrian Horzyk

[CH6] M-CLANN: Multiclass Concept Lattice-Based Artificial

Neural Network

Engelbert Mephu Nguifo, Norbert Tsopze, Gilbert Tindo

[CH7] Constructive Morphological Neural Networks: Some

Theoretical Aspects and Experimental Results in

Classification

Peter Sussner, Estevao Laureano Esmi

[CH8] A Feedforward Constructive Neural Network Algorithm

for Multiclass Tasks Based on Linear Separability

Joao Roberto Bertini Jr., Maria do Carmo Nicoletti

Architectures Suitable for Classification Tasks

Maria do Carmo Nicoletti, Joao R. Bertini Jr., David Elizondo,

Leonardo Franco, Jose M. Jerez

[CH2] Efficient Constructive Techniques for Training Switching

Neural Networks

Enrico Ferrari, Marco Muselli

[CH3] Constructive Neural Network Algorithms That Solve

Highly Non-separable Problems

Marek Grochowski, Wlodislaw Duch

[CH4] On Constructing Threshold Networks for Pattern

Classification.

Martin Anthony

[CH5] Self-Optimizing Neural Network 3

Adrian Horzyk

[CH6] M-CLANN: Multiclass Concept Lattice-Based Artificial

Neural Network

Engelbert Mephu Nguifo, Norbert Tsopze, Gilbert Tindo

[CH7] Constructive Morphological Neural Networks: Some

Theoretical Aspects and Experimental Results in

Classification

Peter Sussner, Estevao Laureano Esmi

[CH8] A Feedforward Constructive Neural Network Algorithm

for Multiclass Tasks Based on Linear Separability

Joao Roberto Bertini Jr., Maria do Carmo Nicoletti

[CH9] Analysis and Testing of the
m-Class RDP Neural Network

David A. Elizondo, Juan M. Ortiz-de-Lazcano-Lobato,

Ralph Birkenhead

[CH10] Active Learning Using a Constructive Neural Network

Algorithm

Jose L. Subirats, Leonardo Franco, Ignacio Molina, Jose M. Jerez

[CH11] Incorporating Expert Advice into Reinforcement Learning

Using Constructive Neural Networks

Robert Ollington, Peter Vamplew, John Swanson

[CH12] A Constructive Neural Network for Evolving a Machine

Controller in Real-Time

Andreas Huemer, David Elizondo, Mario Gongora

[CH13] Avoiding Prototype Proliferation in Incremental Vector

Quantization of Large Heterogeneous Datasets

Hector F. Satizabal, Andres Perez-Uribe, Marco Tomassini

[CH14] Tuning Parameters in Fuzzy Growing Hierarchical

Self-Organizing Networks

Miguel Arturo Barreto-Sanz, Andres Perez-Uribe,

Carlos-Andres Peña-Reyes, Marco Tomassini

[CH15] Self-Organizing Neural Grove: Efficient Multiple Classifier

System with Pruned Self-Generating Neural Trees

Hirotaka Inoue

David A. Elizondo, Juan M. Ortiz-de-Lazcano-Lobato,

Ralph Birkenhead

[CH10] Active Learning Using a Constructive Neural Network

Algorithm

Jose L. Subirats, Leonardo Franco, Ignacio Molina, Jose M. Jerez

[CH11] Incorporating Expert Advice into Reinforcement Learning

Using Constructive Neural Networks

Robert Ollington, Peter Vamplew, John Swanson

[CH12] A Constructive Neural Network for Evolving a Machine

Controller in Real-Time

Andreas Huemer, David Elizondo, Mario Gongora

[CH13] Avoiding Prototype Proliferation in Incremental Vector

Quantization of Large Heterogeneous Datasets

Hector F. Satizabal, Andres Perez-Uribe, Marco Tomassini

[CH14] Tuning Parameters in Fuzzy Growing Hierarchical

Self-Organizing Networks

Miguel Arturo Barreto-Sanz, Andres Perez-Uribe,

Carlos-Andres Peña-Reyes, Marco Tomassini

[CH15] Self-Organizing Neural Grove: Efficient Multiple Classifier

System with Pruned Self-Generating Neural Trees

Hirotaka Inoue