The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Smooth Approximations to Nonlinear Complementarity Problems
SIAM Journal on Optimization
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression
IEEE Transactions on Knowledge and Data Engineering
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Active set support vector regression
IEEE Transactions on Neural Networks
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A new smooth support vector regression based on ε-insensitive logistic loss function, shortly Lε-SSVR, was proposed in this paper, which is similar to SSVR, but without adding any heuristic smoothing parameters and with robust absolute loss. Taking advantage of Lε-SSVR, one can now consider SVM as linear programming, and efficiently solve large-scale regression problems without any optimization packages. Details of this algorithm and its implementation were presented in this paper. Simulation results for both artificial and real data show remarkable improvement of generalization performance and training time.