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Support Vector Regression via Mathematica

Bela Palancz
Organization: Budapest University of Technology and Economics
Department: Photogrammetry and Geoinformatics
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In this tutorial type paper a Mathematica function for Support Vector Regression has been developed. Summarizing the main definitions and theorems of SVR, the detailed implementation steps of this function are presented and its application is illustrated by solving three 2D function approximation test problems, employing a stronger regularized universal Fourier and a wavelet kernel. In addition a real world regression problem, forecasting of the peak of floodwave, is also solved. The numeric - symbolic results show how easily and effectively Mathematica can be used for solving SVR problems.

*Applied Mathematics > Computer Science
*Mathematics > Calculus and Analysis > Harmonic Analysis
*Mathematics > Probability and Statistics

Support Vector Machine, Regression, Approximation, Bela Palancz
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SVMMathematica.nb (2.2 MB) - Mathematica Notebook [for Mathematica 5.0]