L. Franco, D. Elizondo and Jerez, J.M. Editors
BOOk CNN COnstructive Neural Network Franco Elizondo Jerez
ISBN  : 978-3-642-04511-0
Springer Series on Computational Intelligence, Vol. 258

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

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 18th 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
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

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

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