A novel and quick SVM-based multi-class classifier

Use different real positive numbers p"i to represent all kinds of pattern categories, after mapping the inputted patterns into a special feature space by a non-linear mapping, a linear relation between the mapped patterns and numbers p"i is assumed, whose bias and coefficients are undetermined, and the hyper-plane corresponding to zero output of the linear relation is looked as the base hyper-plane. To determine the pending parameters, an objective function is founded aiming to minimize the difference between the outputs of the patterns belonging to a same type and the corresponding p"i, and to maximize the distance between any two different hyper-planes corresponding to different pattern types. The objective function is same to that of support vector regression in form, so the coefficients and bias of the linear relation are calculated by some known methods such as SVM^l^i^g^h^t approach. Simultaneously, three methods are also given to determine p"i, the best one is to determine them in training process, which has relatively high accuracy. Experiment results of the IRIS data set show that, the accuracy of this method is better than those of many SVM-based multi-class classifiers, and close to that of DAGSVM (decision-directed acyclic graph SVM), emphatically, the recognition speed is the highest.

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