Classification is one of the most researched questions in machine learning and data mining.
A wide range of real problems have been stated as classification problems, for example …

The performance of support vector machines in nonlinearly separable classification
problems strongly relies on the kernel function. Toward an automatic machine learning …

In this paper, a new class of Evolutionary Algorithm (EA) named as Genetic Folding (GF) is
introduced. GF is based on novel chromosomes organisation which is structured in a parent …

Abstract Support Vector Machines (SVMs) classification learning is a powerful paradigm to
investigate inverse input-output relationship of a specific problem according to some …

Support Vector Regression (SVR) is an attractive approach for data modeling. The SVR is
based on mapping nonlinear input to a linear in the feature space. Instead of minimizing the …

A method of constructing fuzzy neural network structure by using rough set theory is
presented. Since rough set theory has strong ability of analyzing numerical value and fuzzy …

Abstract Support Vector Machines (SVMs) concern a new generation learning systems
based on recent advances in statistical learning theory. A key problem of these methods is …

The objective of this work is to present a novel evolutionary-based approach that can create
and optimize powerful string kernels using Genetic Programming. The proposed model …

An efficient classification system uses only the most representative features extracted from
images in order to reach a decision. A multimodal system may consider multiple sources of …

Relevance vector machine (RVM) is a state-of-the-art technique for regression and
classification, as a sparse Bayesian extension version of the support vector machine. The …