Pattern Classification and Experiment Testing Based on Core Vector Machine

To handle such kind of large datasets,we use a new kind of CVM variable--Core Vector Machine(CVM)for pattern classification.CVM algorithm formulates original kernel methods in SVM as a Minimum Enclosing Ball(MEB) problems in computational geometry and it can be used with any linear/nonlinear kernels.Experiment shows that the CVM is as accurate as existing SVM implementations,but it gets smaller support vectors and much faster than SVM for very larger datasets.