一类支持向量机在车辆识别中的应用

Vehicle Recognition Based on 1—-SVM

支持向量机分类方法已经在实际应用中显示了良好的学习性能,其最初是针对二值分类问题提出的,如何有效地将支持向量机推广到多值分类中一直是人们关注的课题。通常的多值分类问题是以一系列二值分类来实现,可是这将导致较高的计算复杂性,本文将一类支持向量机推广到多值分类情况,并将其应用于车辆识别中,仿真实验结果表明了所给方法的可行性及有效性。

The Support Vector Machine (SVM) has shown excellent performance in practice as a classification methodology) which were originally designed for binary classification. How to effectively extend SVM for multi-class classification is still an on-going research issue. Oftentimes multi-class classification problem have been treated as a series of binary problems in the SVM paradigm, but it is computationally more expensive to solve multi-class problems. In this paper, a new multi-class classification method is proposed based on one-class support vector machine (1-SVM), and then applies the method to vehicle recognition. The results of simulation experiments at vehicle database show that the proposed method is effective and feasible.