The nature of statistical learning theory
The nature of statistical learning theory
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Support Vector Data Description
Machine Learning
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Bayes classification based on minimum bounding spheres
Neurocomputing
A novel fuzzy compensation multi-class support vector machine
Applied Intelligence
Sharpness preserving image enlargement by using self-decomposed codebook and Mahalanobis distance
Image and Vision Computing
Sphere-structured support vector machines for multi-class pattern recognition
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Robust support vector machine with bullet hole image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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This paper presents a fuzzy support vector classifier by integrating modified fuzzy c-means clustering based on Mahalanobis distance into fuzzy support vector data description. The proposed algorithm can be used to deal with the outlier sensitivity problem in traditional multi-class classification problems. The modified fuzzy c-means clustering algorithm based on Mahalanobis distance takes into the samples' correlation account, and is improved to generate different weight values for main training data points and outliers according to their relative importance in the training data. Experimental results show that the proposed method can reduce the effect of outliers and give high classification accuracy.