Vehicle Classification Algorithm Based on Fuzzy SVM Models
As a typical binary classifier, its an inseparable sample problem about the Support Vector Machine (SVM) when processing the classification of the multi-class vehicle models. Since the SVM can not estimate the effect size of the samples classification accurately, and then reduces the classification generalization ability. In this paper, a fuzzy Support Vector Machine (FSVM) classification algorithm is applied to vehicle classification. According to the difference of the contribution which the vehicle characteristics make to the classification, the appropriate degree of membership is given, and the algorithm improves the vehicle models classification ability of the traditional SVM effectively. The experimental results show that the new method, compared with the existing vehicle classification method, is feasible, effective, and with a high classification accuracy
Multiclass classification of leukemia cancer data using Fuzzy Support Vector Machine (FSVM) with feature selection using Principal Component Analysis (PCA)
