Since the existing methods can not balance the sufficient use of information and the scale of the optimization problem, a new method for multi class classification problem is proposed, which is called multi-class support vector machine based on the minimization of class variance (MCVMSVM for short). MCVMSVM adopts the idea of semi-supervised learning and transfers the K-class problem to K(K − 1)/2 binary classification problems. For each binary classification problem, a new SVM with a mixed regularization term which considers the margin and the distribution of examples is proposed. MCVMSVM can utilize the information of all examples without increasing the scale of the optimization problem. The performance of MCVMSVM on UCI and NDC datasets is the best compared with other methods, that means MCVMSVM is more effective.

由于现有方法无法平衡信息的充分利用和优化问题的规模，因此提出了一种新的多类分类问题方法，即基于类方差最小化的多类支持向量机（MCVMSVM）。短）。MCVMSVM采用半监督学习的思想，并将K类问题转换为K（K − 1）/ 2个二元分类问题。对于每个二进制分类问题，提出了一种新的支持向量化的支持向量机，该支持向量考虑了边距和示例分布。MCVMSVM可以利用所有示例的信息，而不会增加优化问题的规模。与其他方法相比，MCVMSVM在UCI和NDC数据集上的性能最佳，这意味着MCVMSVM更有效。