epsilon-Proximal support vector machine for binary classification and its application in vehicle recognition

摘要

In this paper, we propose a novel proximal support vector machine (PSVM), named epsilon-proximal support vector machine (epsilon-PSVM), for binary classification. By introducing the epsilon-insensitive loss function instead of the quadratic loss function into PSVM, the proposed epsilon-PSVM has several improved advantages compared with the traditional PSVM: (1) It is sparse controlled by the parameter epsilon. (2) It is actually a kind of epsilon-support vector regression (epsilon-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 e for each class, unbalanced problem can be solved successfully, furthermore, a useful choice of the parameter epsilon 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. (C) 2015 Elsevier B.V. All rights reserved.