Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
A tutorial on ν-support vector machines: Research Articles
Applied Stochastic Models in Business and Industry - Statistical Learning
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Sparse probabilistic classifiers
Proceedings of the 24th international conference on Machine learning
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Condensed vector machines: learning fast machine for large data
IEEE Transactions on Neural Networks
INFORMS Journal on Computing
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In a recently published paper in JMLR, Tsang et al. (2005) present an algorithm for SVM called Core Vector Machines (CVM) and illustrate its performances through comparisons with other SVM solvers. After reading the CVM paper we were surprised by some of the reported results. In order to clarify the matter, we decided to reproduce some of the experiments. It turns out that to some extent, our results contradict those reported. Reasons of these different behaviors are given through the analysis of the stopping criterion.