Improved Design for Hardware Implementation of Graph-Based Large Margin Classifiers for Embedded Edge Computing.

The number of connected embedded edge computing Internet of Things (IoT) devices has been increasing over the years, contributing to the significant growth of available data in different scenarios. Thereby, machine learning algorithms arise to enable task automation and process optimization based on those data. However, due to some learning methods' computational complexity implementing geometric classifiers, it is a challenge to map these on embedded systems or devices with limited resources in size, processing, memory, and power, to accomplish the desired requirements.

Large Margin Gaussian Mixture Classifier With a Gabriel Graph Geometric Representation of Data Set Structure.

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 Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometrical support vectors (SVs) (analogous to support vector machines (SVMs) SVs) are obtained in order to yield the final large margin solution from a Gaussian mixture model.

Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data.

Traditional learning algorithms applied to complex and highly imbalanced training sets may not give satisfactory results when distinguishing between examples of the classes. The tendency is to yield classification models that are biased towards the overrepresented (majority) class. This paper investigates this class imbalance problem in the context of multilayer perceptron (MLP) neural networks.