Making large-scale support vector machine learning practical
Advances in kernel methods
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"
The Journal of Machine Learning Research
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
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This paper introduces Sphere Support Vector Machines (SVMs) as the new fast classification algorithm based on combining a minimal enclosing ball approach, state of the art nearest point problem solvers and probabilistic techniques. The blending of the three significantly speeds up the training phase of SVMs and also attains practically the same accuracy as the other classification models over several large real datasets within the strict validation frame of a double (nested) cross-validation. The results shown are promoting SphereSVM as outstanding alternatives for handling large and ultra-large datasets in a reasonable time without switching to various parallelization schemes for SVM algorithms recently proposed.