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Volume 10, Issue 1


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A Flexible Implementation for Support Vector Machines
Roland Nilsson
Johan Björkegren
Jesper Tegnér

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.





*Support Vector Machines

*Solving the Optimization Problem

*Feature Space and Kernels

*Regression Analysis with SVMs



*Additional Material

About the Authors
Roland Nilsson is a graduate student at Linköping University working with machine learning algorithms in analysis of high-dimensional biomedical data.

Johan Björkegren is an associate professor in molecular medicine at Karolinska Institutet, Sweden, and cofounder of Clinical Gene Networks, a biotechnology company involved in system-level analysis of biomedical data in cardiovascular disease.

Jesper Tegnér is a professor of computational biology at Linköping University, Sweden, and cofounder of Clinical Gene Networks.

Roland Nilsson
Computational Biology
Linköping University
SE-58183 Linköping, Sweden

Johan Björkegren
Center for Genomics and Bioinformatics
Karolinska Institutet
SE-17177 Stockholm, Sweden

Jesper Tegnér
Computational Biology
Linköping University
SE-58183 Linköping, Sweden

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