Learning curves provide insight into the dependence of a learner's generalization
performance on the training set size. This important tool can be used for model selection, to …

Feedforward neural networks trained by error backpropagation are examples of
nonparametric regression estimators. We present a tutorial on nonparametric inference and …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Abstract Neural Networks (NNs) have provided state-of-the-art results for many challenging
machine learning tasks such as detection, regression and classification across the domains …

Deep learning (DL) has revolutionized the field of computer vision and image processing. In
medical imaging, algorithmic solutions based on DL have been shown to achieve high …

Recently, techniques for applying convolutional neural networks to graph-structured data
have emerged. Graph convolutional neural networks (GCNNs) have been used to address …

Reliable and accurate probabilistic forecasting of wind speed is of vital importance for the
utilization of wind energy and operation of power systems. In this paper, a probabilistic …

The ability of learning networks to generalize can be greatly enhanced by providing
constraints from the task domain. This paper demonstrates how such constraints can be …

A training algorithm that maximizes the margin between the training patterns and the
decision boundary is presented. The technique is applicable to a wide variety of the …

We present an application of back-propagation networks to hand (cid: 173) written digit
recognition. Minimal preprocessing of the data was required, but architecture of the network …