A new algorithm that combined Support Vector Machine (SVM) with

K

Nearest neighbour (

K

NN) is presented and it comes into being a new classifier.The classifier based on taking SVM as a 1NN classifier in which only one representative point is selected for each class.In the class phase

the algorithm computes the distance from the test sample to the optimal super-plane of SVM in feature space.If the distance is greater than the given threshold

the test sample would be classified on SVM;otherwise

the

K

NN algorithm will be used.In

K

NN algorithm

we select every support vector as representative point and compare the distance between the testing sample and every support vector.The testing sample can be classed by finding the k-nearest neighbour of testing sample.The numerical experim

ents show that the mixed algorithm can not only improve the accuracy compared to sole SVM

but also better solve the problem of selecting the parameter of kernel function for SVM.