Support vector machines(SVM) take very long time in training and support vector domain classifier(SVDC) has the disadvantage of low accuracy in binary classification problem.A separation hyperplane is constructed based on support vector domain description(SVDD)

which attempts the combination of SVDD with SVM.Firstly

two upper bounds for two-class error ratio are made equal so as to determinate the two parameter values C1

C2.Then

through description of the training samples from each class respectively using SVDD

two-class hypersphere boundary vectors are obtained.Finally

the normal vector of the separation hyperplane is deduced by the difference of the two hypersphere centers based on the rule of the distance sum maximization of the two hypersphere centers to the separation hyperplane.The threshold is determined to construct a separation hyperplane by the information provided by the sample size and radii of the two hyperspheres.Real experimental data show that the error ratio of the proposed algorithm decreases greatly than that of SVDC and SVM

and the training time decreases largely.