Abstract
Semi-supervised Support Vector Data Description multi-classification algorithm is presented, in order to solve less labeled data learning, difficulties in the implementation and poor results of semi-supervised multi-classification, which full use the distribution of information in of non-target samples. S3VDD-MC algorithm defines the degree of membership of non-target samples, in order to get the non-target samples’ accepted labels or refused labels, on this basis, several super-spheres constructed, a k-classification problem is transformed into k SVDDs problem. Finally, the simulation results verify the effectiveness of the algorithm.
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