Orthogonal polynomial kernels have been recently introduced to enhance support vector
machine classifiers by reducing their number of support vectors. Previous works have …

Purpose: Magnetic resonance imaging (MRI) has been proposed as a promising alternative
to transrectal ultrasound for the detection and localization of prostate cancer and fusing the …

This paper proposes an approach for warnings and failures detection based on fuzzy
clustering and the Mahalanobis distance. Both techniques are developed in a real wind farm …

In practical engineering, small-scale data sets are usually sparse and contaminated by
noise. In this paper, we propose a new sequence of orthogonal polynomials varying with …

Orthogonal functions have many useful properties and can be used for different purposes in
machine learning. One of the main applications of the orthogonal functions is producing …

Meta-learning aims to apply existing models on new tasks where the goal is “learning to
learn” so that learning from a limited amount of labeled data or learning in a short amount of …

Abstract Support Vector Machines (SVM) can perform very well on noise free data sets and
can usually achieve good classification accuracies when the data is noisy. However …

Support vector regression machine (SVR) has become a promising tool in many research
fields, such as web intelligence, machinery fault diagnostic technique, dynamics …

A kernel function is an important component in the support vector machine (SVM) kernel-
based classifier. This is due to the elegant mathematical characteristics of a kernel, which …

Support vector regression machine (SVR) has become a promising tool in many research
fields, such as web intelligence, machinery fault diagnostic technique, dynamics …