The Research on Semi-Supervised Support Vector Data Description Multi-Classification Algorithm
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.
D. Q. Xue "The Research on Semi-Supervised Support Vector Data Description Multi-Classification Algorithm", Advanced Materials Research, Vols. 268-270, pp. 1115-1120, 2011