SUPPORT VECTOR CLASSIFIER VIA MATHEMATICA
Béla Paláncz, Lajos Völgyesi · 2005
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.