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 …

Neural network-based modeling often involves trying multiple networks with different
architectures and training parameters in order to achieve acceptable model accuracy …

Artificial neural networks (ANNs) are comparatively straightforward to understand and use in
the analysis of scientific data. However, this relative transparency may encourage their use …

Continual learning is the constant development of complex behaviors with no final end in
mind. It is the process of learning ever more complicated skills by building on those skills …

A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S1-Systole-
S2-Diastole) using homomorphic filtering and K-means clustering is presented. Feature …

Constructive learning algorithms offer an attractive approach for the incremental construction
of near-minimal neural-network architectures for pattern classification. They help overcome …

In this paper, we study a number of objective functions for training new hidden units in
constructive algorithms for multilayer feedforward networks. The aim is to derive a class of …

Lazy learning methods like the k-nearest neighbor classifier require storing the whole
training set and may be too costly when this set is large. The condensed nearest neighbor …

The multilayer perceptron (MLP) neural network is interpreted from the geometrical
viewpoint in this work, that is, an MLP partition an input feature space into multiple …

In this paper, an optimized approximation algorithm (OAA) is proposed to address the
overfitting problem in function approximation using neural networks (NNs). The optimized …