'Periodica Polytechnica Budapest University of Technology and Economics'
Abstract
In this case study a Support Vector Classifier function has been developed
in Mathematica. Starting with a brief summary of support vector classification method,
the step by step implementation of the classification algorithm in
Mathematica is presented and explained. To check our function, two test problems,
learning a chess board and classification of two intertwined spirals are
solved. In addition, an application to filtering of airborne digital land
image by pixel classification is demonstrated using a new SVM kernel family,
the KMOD, a kernel with moderate decreasing
