The SVM Based on Reduction Strategy Applied in Bus Passenger Flow Counting

Abstract

There has become a bottleneck to use support vector machine (SVM) due to the problems such as slow learning speed, large buffer memory requirement, low generalization performance and so on. These problems are caused by large-scale training sample set and outlier data immixed in the other class. Aiming at these problems, this paper proposed a new reduction strategy for large-scale training sample set according to analyzing on the structure of the training sample set based on the point set theory. By using fuzzy clustering method in this new strategy, the potential support vectors are obtained and the non-boundary outlier data immixed in the other class is removed. In view of reducing greatly the scale of the training sample set, it improves the generalization performance of SVM and effectively avoids over-learning. Then the proposed method was applied to bus passenger flow counting. The experimental results show that the method reposed in this paper obtains higher classification accuracy.