Constructive Neural Networks

Constructive neural networks and other incremental learning algorithms are discussed in this volume as alternatives to methods for assessing adequate architectures. A valuable overview of the field is presented, in addition to useful applications.

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.

Presents the state of the art of Constructive Algorithms for Neural Networks

Klappentext

The book is a collection of invited papers on Constructive methods for Neural networks. Most of the chapters are extended versions of works presented on the special session on constructive neural network algorithms of the 18th International Conference on Artificial Neural Networks (ICANN 2008) held 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.

Inhalt
Constructive Neural Network Algorithms for Feedforward Architectures Suitable for Classification Tasks.- Efficient Constructive Techniques for Training Switching Neural Networks.- Constructive Neural Network Algorithms That Solve Highly Non-separable Problems.- On Constructing Threshold Networks for Pattern Classification.- Self-Optimizing Neural Network 3.- M-CLANN: Multiclass Concept Lattice-Based Artificial Neural Network.- Constructive Morphological Neural Networks: Some Theoretical Aspects and Experimental Results in Classification.- A Feedforward Constructive Neural Network Algorithm for Multiclass Tasks Based on Linear Separability.- Analysis and Testing of the m-Class RDP Neural Network.- Active Learning Using a Constructive Neural Network Algorithm.- Incorporating Expert Advice into Reinforcement Learning Using Constructive Neural Networks.- A Constructive Neural Network for Evolving a Machine Controller in Real-Time.- Avoiding Prototype Proliferation in Incremental Vector Quantization of Large Heterogeneous Datasets.- Tuning Parameters in Fuzzy Growing Hierarchical Self-Organizing Networks.- Self-Organizing Neural Grove: Efficient Multiple Classifier System with Pruned Self-Generating Neural Trees.