Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A parallel mixture of SVMs for very large scale problems
Neural Computation
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Lagrangian support vector machines
The Journal of Machine Learning Research
Decomposition methods for linear support vector machines
Neural Computation
Active set support vector regression
IEEE Transactions on Neural Networks
Efficient kernel feature extraction for massive data sets
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Brief paper: On-line voltage security assessment of power systems using core vector machines
Engineering Applications of Artificial Intelligence
Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
ACM Transactions on Algorithms (TALG)
Ordinal-class core vector machine
Journal of Computer Science and Technology
Diversified SVM ensembles for large data sets
ECML'06 Proceedings of the 17th European conference on Machine Learning
Rough margin based core vector machine
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
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In this paper, we extend the recently proposed Core Vector Machine algorithm to the regression setting by generalizing the underlying minimum enclosing ball problem. The resultant Core Vector Regression (CVR) algorithm can be used with any linear/nonlinear kernels and can obtain provably approximately optimal solutions. Its asymptotic time complexity is linear in the number of training patterns m, while its space complexity is independent of m. Experiments show that CVR has comparable performance with SVR, but is much faster and produces much fewer support vectors on very large data sets. It is also successfully applied to large 3D point sets in computer graphics for the modeling of implicit surfaces.