Data mining techniques often ask for the resolution of optimization problems. Supervised
classification, and, in particular, support vector machines, can be seen as a paradigmatic …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Proper parameter settings of support vector machine (SVM) and feature selection are of
great importance to its efficiency and accuracy. In this paper, we propose a parallel time …

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 …

This study; i) investigates the suitability of two frequently employed machine learning
algorithms in remote sensing, namely, random forests (RFs) and support vector regression …

The support vector classifier (SVC) is one of the most powerful machine learning algorithms.
This algorithm has been accepted as an effective method in three-dimensional geological …

La tâche dans les présentes expériences était d'atteindre et de saisir un objet nouveau
offrant deux prises possibles. Différentes versions de l'objet ont été crées, influençant le …

This paper investigates the potential of a Gaussian process (GP) regression approach to
predict the load-bearing capacity of piles. Support vector machines (SVM) and empirical …

Learning from data is a very attractive alternative to “manually” learning. Therefore, in the
last decade the use of machine learning has spread rapidly throughout computer science …

This paper hybridizes the gravitational search algorithm (GSA) with support vector machine
(SVM) and makes a novel GSA-SVM hybrid system to improve classification accuracy with …