Abstract: A classification algorithm of non-linear support vector machine (SVM) decision tree is presented. The presented method extends the SVM to the non-linear SVM by using kernel functions and calculates the relativity separability measure between classes after non-linear mapping. This method is introduced on the basis of the SVM decision tree. As a result, the iterative error is effectively restrained and the efficiency improved accordingly. Experimental results have shown that, compared with the original SVM decision tree algorithm, the classification rate has increased greatly and that the classification time decreased apparently.

Key words: classification, support vector machine (SVM), kernel function, decision tree