The kernelized one-class hard-margin SVDD, the kernelized soft-margin one-class and two-class SVMs, the kernelized L2-support vector regression, and the kernelized Ranking SVM can be proved to be the center-constrained minimum enclosing ball(CCMEB) problem. Therefore, a kernelized two-class L2-SVM with multi-view(multi-view L2- SVM) is equivalently formulated as the CCMEB problem, and a classification method named multi-view core vector machine(MvCVM) is proposed. Both the proposed multi-view L2-SVM and MvCVM classifiers can obtain an overall consensus classification result on each view because the differences and the associations between different views are both considered in the two proposed models. An extensive set of experiments on synthetic and real-world multi-view datasets are conducted to demonstrate the effectiveness of the proposed methods.