An easily implementable mixed-integer algorithm is proposed that generates a nonlinear kernel support vector machine (SVM) classifier with reduced input space features. A single ...
This work proposes the use of maximal variation analysis for feature selection within least squares support vector machines for survival analysis. Instead of selecting a subset of ...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
In this study, we propose an improved semi-supervised support vector machine (SVM) based translation algorithm for brain-computer interface (BCI) systems, aiming at reducing the t...
— A heuristic is proposed to address free parameter selection for Support Vector Machines, with the goals of improving generalization performance and providing greater insensitiv...
Abstract— Support vector machines are very accurate classifiers and have been widely used in many applications. However, the training and to a lesser extent prediction time of s...
Tong Luo, Lawrence O. Hall, Dmitry B. Goldgof, And...