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ORCIDConstructive Neural Networks 2009
- Leonardo Franco, David A. Elizondo, José M. Jerez:
Constructive Neural Networks. Studies in Computational Intelligence 258, Springer 2009, ISBN 978-3-642-04511-0
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 Gongora:
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 Andrés 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
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