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.

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.