Constructive Neural Networks

Front Cover
Leonardo Franco, Josť M. Jerez
Springer Science & Business Media, Oct 27, 2009 - Mathematics - 293 pages
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.
 

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Contents

Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks
1
Efficient Constructive Techniques for Training Switching Neural Networks
25
Constructive Neural Network Algorithms That Solve Highly Nonseparable Problems
49
On Constructing Threshold Networks for Pattern Classification
71
SelfOptimizing Neural Network 3
83
Multiclass Concept LatticeBased Artificial Neural Network
102
Some Theoretical Aspects and Experimental Results in Classification
123
A Feedforward Constructive Neural Network Algorithm for Multiclass Tasks Based on Linear Separability
145
Active Learning Using a Constructive Neural Network Algorithm
193
Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks
207
A Constructive Neural Network for Evolving a Machine Controller in RealTime
225
Avoiding Prototype Proliferation in Incremental Vector Quantization of Large Heterogeneous Datasets
243
Tuning Parameters in Fuzzy Growing Hierarchical SelfOrganizing Networks
261
Efficient Multiple Classifier System with Pruned SelfGenerating Neural Trees
281
Author Index
292
Copyright

Analysis and Testing of the mClass RDP Neural Network
170

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