A Flexible Implementation for Support Vector Machines
Roland Nilsson, Johan Björkegren, Jesper N. Tegner · The Mathematica Journal · 2006
Support vector machines (SVMs) are learning algorithms that have many applications in pattern recognition and nonlinear regression. Being very popular, SVM software is available in many versions. Still, existing implementations, usually in low-level languages such as C, are often difficult to understand and adapt to specific research tasks. In this article, we present a compact and yet flexible implementation of SVMs in Mathematica, traditionally named MathSVM. This software is designed to be easy to extend and modify, drawing on the powerful high-level language of Mathematica.