Design of Support Vector Machines Using Cross Distance Minimization Algorithm

According to the advantages of kernel method in dealing with non-linear separable data,a hard margin non-kernel support vector machine,i.e.Cross Distance Minimization Algorithm(CDMA) to its kernel version,called KCDMA,was extended.Firstly,CDMA was expressed as the form of inner product,and kernel function was introduced to replace inner product.After that,by using quadratic cost,CDMA was generalized to its extension,namely,KCDMA.KCDMA was applicable in the nonlinear case and allowed violations to classify non-separable data sets.Resultsshow that this method is totally very competitive with some well-known and powerful support vector machines.