Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
IEEE Transactions on Neural Networks
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Classic MEB (minimum enclosing ball) models characteristics of each class for classification by extracting core vectors through a (1 + ε)-approximation problem solving. In this paper, we develop a new MEB systemlearning the core vectors set in a group manner, called group MEB (g-MEB). The g-MEB factorizes class characteristic in 3 aspects such as, reducing the sparseness in MEB by decomposing data space based on data distribution density, discriminating core vectors on class interaction hyperplanes, and enabling outliers detection to decrease noise affection. Experimental results show that the factorized core set from g-MEB delivers often apparently higher classification accuracies than the classic MEB.