?-Proximal support vector machine for binary classification and its application in vehicle recognition
In this paper, we propose a novel proximal support vector machine (PSVM), named ?-proximal support vector machine (?-PSVM), for binary classification. By introducing the ?-insensitive loss function instead of the quadratic loss function into PSVM, the proposed ?-PSVM has several improved advantages compared with the traditional PSVM: (1) It is sparse controlled by the parameter ?. (2) It is actually a kind of ?-support vector regression (?-SVR), the only difference here is that it takes the binary classification problem as a special kind of regression problem. (3) By weighting different sparseness parameter ? for each class, unbalanced problem can be solved successfully, furthermore, a useful choice of the parameter ? is proposed. (4) It can be solved efficiently for large scale problems by the Successive Over relaxation (SOR) technique. Experimental results on several benchmark datasets show the effectiveness of our method in sparseness, balance performance and classification accuracy, and therefore confirm the above conclusion further. At last, we also apply this new method to the vehicle recognition and the results show its efficiency.
proximal support vector machines classification ?-insensitive loss function sparseness regression