New type of support vector machine by moving separating hyperplane

For binary pattern recognition problem, the canonical support vector machine put forward by Vapnik didn´t distinguish two classification errors appearing in classifying two sample sets. So a new method, asymmetrical support vector machine (A-SVM), is proposed. The optimal separating hyperplane was deviated from the optimal support hyperplane of some kind of sample set by parallel moving the optimal separating hyperplane, and then this kind of sample set could be recognized with higher accuracy. Simulation example shows that A-SVM is similar to SVM for the total recognizing performance of both learning and testing. However, A-SVM is better than SVM when separating the kind of sample set