While large margin classifiers are originally an outcome of an optimization framework,
support vectors (SVs) can be obtained from geometric approaches. This article presents …

Detecting anomalies in industrial processes is a critical task. Prior fault detection can reduce
company costs, and most importantly, may prevent accidents and environmental damage …

This work introduces a structural approach to addressing online learning problems by
leveraging dataset relationships to represent the problem and estimate the likelihoods for a …

This brief presents a geometrical approach for obtaining large margin classifiers. The
method aims at exploring the geometrical properties of the data set from the structure of a …

This paper presents the concept of Graph-based Local Resampling of perceptron-like neural
networks with random projections (RN-ELM) which aims at regularization of the yielded …

The definition of an appropriate number of Radial Basis Functions and their parameters in
Radial Basis Function networks is a non-trivial task. The fitting of its parameters has direct …

This work presents a new decision-making strategy for multi-objective learning problem of
artificial neural networks (ANN). The proposed decision-maker searches for the solution that …

It is well known that there is an increasing interest in edge computing to reduce the distance
between cloud and end devices, especially for machine learning (ML) methods. However …

In this paper we propose the development of an approach capable of improving the results
obtained by classification algorithms when applied to imbalanced datasets. The method …

This work presents a methodology directed to pattern classification problems. The goal is to
design large margin classifiers where the information necessary is obtained from the …