Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Making large-scale support vector machine learning practical
Advances in kernel methods
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Scale Kernel Regression via Linear Programming
Machine Learning
Convex Hull in Feature Space for Support Vector Machines
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A new infeasible interior-point algorithm for linear programming
Proceedings of the 2003 conference on Diversity in computing
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A tutorial on support vector regression
Statistics and Computing
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Large-scale Learning with SVM and Convolutional for Generic Object Categorization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Sparse correlation kernel reconstruction
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Neighborhood Preprocessing SVM for Large-Scale Data Sets Classification
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Using Numerical Simplification to Control Bloat in Genetic Programming
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Mathematics and Computers in Simulation
A novel kernel-based maximum a posteriori classification method
Neural Networks
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
On the sparseness of 1-norm support vector machines
Neural Networks
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Working Set Selection Using Functional Gain for LS-SVM
IEEE Transactions on Neural Networks
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This paper presents a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution becomes, and more computational efficiency can be gained in comparison with other methods. This demonstrates that the proposed learning scheme and the LP-SVR model are robust and efficient when compared with other methodologies for large-scale problems.