In this survey paper, we review the constructive algorithms for structure learning in
feedforward neural networks for regression problems. The basic idea is to start with a small …

The problem of dimension reduction is introduced as a way to overcome the curse of the
dimensionality when dealing with vector data in high-dimensional spaces and as a …

The lack of interpretability is an inevitable problem when using neural network models in
real applications. In this paper, an explainable neural network based on generalized …

This book provides the first comprehensive treatment of feed-forward neural networks from
the perspective of statistical pattern recognition. After introducing the basic concepts of …

Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …

Artificial neural networks are nonlinear map** systems whose structure is loosely based
on principles observed in the nervous systems of humans and animals. The basic idea is …

The deterministic annealing approach to clustering and its extensions has demonstrated
substantial performance improvement over standard supervised and unsupervised learning …

We introduce a new class of nonstationary covariance functions for spatial modelling.
Nonstationary covariance functions allow the model to adapt to spatial surfaces whose …

A wavelet-based neural network is described. The structure of this network is similar to that
of the radial basis function (RBF) network, except that in the present paper the radial basis …

Feed‐forward neural networks are now widely used in classification problems, whereas non‐
linear methods of discrimination developed in the statistical field are much less widely …