Nonlinear complementarity as unconstrained and constrained minimization
Mathematical Programming: Series A and B - Special issue: Festschrift in Honor of Philip Wolfe part II: studies in nonlinear programming
The nature of statistical learning theory
The nature of statistical learning theory
Using support vector machines for time series prediction
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Lagrangian support vector machines
The Journal of Machine Learning Research
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least squares twin support vector machines for pattern classification
Expert Systems with Applications: An International Journal
TSVR: An efficient Twin Support Vector Machine for regression
Neural Networks
Training twin support vector regression via linear programming
Neural Computing and Applications - Special Issue on Theory and applications of swarm intelligence
Smooth twin support vector regression
Neural Computing and Applications
Active set support vector regression
IEEE Transactions on Neural Networks
A rough margin-based ν-twin support vector machine
Neural Computing and Applications - Special Issue on LSMS2010 and ICSEE 2010
Finite Newton method for implicit Lagrangian support vector regression
International Journal of Knowledge-based and Intelligent Engineering Systems
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A new smoothing approach for the implicit Lagrangian twin support vector regression is proposed in this paper. Our formulation leads to solving a pair of unconstrained quadratic programming problems of smaller size than in the classical support vector regression and their solutions are obtained using Newton-Armijo algorithm. This approach has the advantage that a system of linear equations is solved in each iteration of the algorithm. Numerical experiments on several synthetic and real-world datasets are performed and, their results and training time are compared with both the support vector regression and twin support vector regression to verify the effectiveness of the proposed method.