Abstract A multisurface support vector machine classifier is proposed called multisurface support vector machines via weight vector projection. It generates two weight vectors by solving two simple eigenvalue problems without consideration of the matrix singularity in it. Unlike the standard classifiers, the solution of the specific hyperplane is not required. According to the decision rule of the proposed approach, a unseen point is assigned to the closest projected mean. The proposed approach obtains comparable computational efficiency compared with proximal support vector machine via generalized eigenvalues (GEPSVM). Moreover, it solves some complex XOR problems as well. The experimental results on artificial and UCI datasets show that the classification performance of the proposed approach outperforms that of GEPSVM.
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2010, Vol. 23 
