Support vector machines based on subtractive clustering

Support vector machines combining subtractive clustering method are proposed in this paper. Subtractive clustering method is used to select a set of cluster centers which are the data samples themselves as the representation of original massive set of training data. The new training set then is used to construct support vector machines. Two benchmarks on two-class recognition and multi-class problem are tested, and the results show that the support vector machines based on subtractive clustering have better or equal classification accuracy and generalization ability with smaller set of training data and cost less optimization computation time than conventional support vector machines.