An ameliorated algorithm that combined support vector machine (SVM) with k nearest neighbour (kNN) is presented and it comes into being as a new classifier

based on the research that SVM classifies some tested samples in error nearby the optimal super-plane.In the class phase

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

the tested sample will be classified on SVM

otherwise

the kNN algorithm will be used based on the best distance measurement.The numerical experiments show that the mixed algorithm improve the accuracy compared to the sole SVM.