Fast support vector machine classification using linear SVMs

We propose a classification method based on a decision tree whose nodes consist of linear Support Vector Machines (SVMs). Each node defines a decision hyperplane that clas-sifies part of the feature space. For large classification prob-lems (with many Support Vectors (SVs)) it has the advantage that the classification time does not depend on the num-ber of SVs. Here, the classification of a new sample can be calculated by the dot product with the orthogonal vec-tor of each hyperplane. The number of nodes in the tree has shown to be much smaller than the number of SVs in a non-linear SVM, thus, a significant speedup in classification time can be achieved. For non-linear separable problems, the trivial solution (zero vector) of a linear SVM is analyzed and a new formulation of the optimization problem is given to avoid it. 1