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.
