Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Core Vector Regression for very large regression problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Neural Computation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multiclass core vector machine
Proceedings of the 24th international conference on Machine learning
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
A rough margin based support vector machine
Information Sciences: an International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
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The recently proposed rough margin based support vector machine (RMSVM) could tackle the overfitting problem due to outliers effectively with the help of rough margins. However, the standard solvers for them are time consuming and not feasible for large datasets. On the other hand, the core vector machine (CVM) is an optimization technique based on the minimum enclosing ball that can scale up an SVM to handle very large datasets. While the 2-norm error used in the CVM might make it theoretically less robust against outliers, the rough margin could make up this deficiency. Therefore we propose our rough margin based core vector machine algorithms. Experimental results show that our algorithms hold the generalization performance almost as good as the RMSVM on large scale datasets and improve the accuracy of the CVM significantly on extremely noisy datasets, whilst cost much less computational resources and are often faster than the CVM.