Numerous problems encountered in real life cannot be actually formulated as a single
objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen …

This article reviews meta-learning also known as learning-to-learn which seeks rapid and
accurate model adaptation to unseen tasks with applications in highly automated AI, few …

Abstract Parameters Selection Problem (PSP) is a relevant and complex optimization issue
in Support Vector Machine (SVM) and Support Vector Regression (SVR), looking for …

Cyber security in the context of big data is known to be a critical problem and presents a
great challenge to the research community. Machine learning algorithms have been …

Support vector machine (SVM) is a popular machine learning method with a solid theoretical
foundation, and has shown promising performance on different classification problems …

Abstract The Support Vector Machine (SVM) is one of the most powerful algorithms for
machine learning and data mining in numerous and heterogenous application domains …

Classification is one of the most well-known tasks in supervised learning. A vast number of
algorithms for pattern classification have been proposed so far. Among these, support vector …

Abstract Support Vector Machines (SVMs) have become a well succeeded technique due to
the good performance it achieves on different learning problems. However, the SVM …

The goal of transfer learning (TL) is providing a framework for exploiting acquired
knowledge from source to target data. Transfer learning approaches compared to traditional …

Real-world classification problems generally deal with imbalanced data, where one class
represents the majority of the data set. The present work deals with event detection on a …