Constructive Neural Networks 2009

This book presents a collection of invited works that consider constructive methods for neural networks, taken primarily from papers presented at a special session held during the 18th International Conference on Artificial Neural Networks (ICANN 2008) in September 2008 in Prague, Czech Republic.

• Maria do Carmo Nicoletti, João Roberto Bertini Jr., David A. Elizondo, Leonardo Franco, José M. Jerez:
  Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks. 1-23
  
• Enrico Ferrari, Marco Muselli:
  Efficient Constructive Techniques for Training Switching Neural Networks. 25-48
  
• Marek Grochowski, Wlodzislaw Duch:
  Constructive Neural Network Algorithms That Solve Highly Non-separable Problems. 49-70
  
• Martin Anthony:
  On Constructing Threshold Networks for Pattern Classification. 71-82
  
• Adrian Horzyk:
  Self-Optimizing Neural Network 3. 83-101
  
• Engelbert Mephu Nguifo, Norbert Tsopzé, Gilbert Tindo:
  M-CLANN: Multiclass Concept Lattice-Based Artificial Neural Network. 103-121
  
• Peter Sussner, Estevão Laureano Esmi:
  Constructive Morphological Neural Networks: Some Theoretical Aspects and Experimental Results in Classification. 123-144
  
• João Roberto Bertini Jr., Maria do Carmo Nicoletti:
  A Feedforward Constructive Neural Network Algorithm for Multiclass Tasks Based on Linear Separability. 145-169
  
• David A. Elizondo, Juan Miguel Ortiz-de-Lazcano-Lobato, Ralph Birkenhead:
  Analysis and Testing of the m-Class RDP Neural Network. 171-192
  
• José Luis Subirats, Leonardo Franco, Ignacio Molina, José M. Jerez:
  Active Learning Using a Constructive Neural Network Algorithm. 193-206
  
• Robert Ollington, Peter Vamplew, John Swanson:
  Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks. 207-224
  
• Andreas Huemer, David A. Elizondo, Mario A. Góngora:
  A Constructive Neural Network for Evolving a Machine Controller in Real-Time. 225-242
  
• Héctor F. Satizábal, Andrés Pérez-Uribe, Marco Tomassini:
  Avoiding Prototype Proliferation in Incremental Vector Quantization of Large Heterogeneous Datasets. 243-260
  
• Miguel Arturo Barreto-Sanz, Andrés Pérez-Uribe, Carlos Andres Peña-Reyes, Marco Tomassini:
  Tuning Parameters in Fuzzy Growing Hierarchical Self-Organizing Networks. 261-279
  
• Hirotaka Inoue:
  Self-Organizing Neural Grove: Efficient Multiple Classifier System with Pruned Self-Generating Neural Trees. 281-291