The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Candidate Vectors Selection for Training Support Vector Machines
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Robust support vector machine with bullet hole image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
Classification in a normalized feature space using support vector machines
IEEE Transactions on Neural Networks
Fuzzy one-class classification model using contamination neighborhoods
Advances in Fuzzy Systems
Fuzzy classifier based on fuzzy support vector machine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In dealing with the Two-Class classification problems, the traditional support vector machine (SVM) often cannot achieve good classification accuracy when outliers exist in the training data set. The fuzzy support vector machine (FSVM) can resolve this problem with an appropriate fuzzy membership for each data point. The effect of the outliers can be effectively reduced when the classification problem is solved. In this paper, a new fuzzy membership function is employed in the linear and nonlinear fuzzy support vector machine respectively. The fuzzy membership is calculated based on the structural information of two classes in the input space and in the feature space. This method can distinguish the support vectors and the outliers effectively. Experimental results show that this approach contributes greatly to the reduction of the effect of the outliers and significantly improves the classification accuracy and generalization.