The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
Polyhedral separability through successive LP
Journal of Optimization Theory and Applications
Lagrangian support vector machines
The Journal of Machine Learning Research
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
SIAM Journal on Optimization
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
An efficient DCA for spherical separation
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Margin maximization in spherical separation
Computational Optimization and Applications
Binary classification via spherical separator by DC programming and DCA
Journal of Global Optimization
A class of semi-supervised support vector machines by DC programming
Advances in Data Analysis and Classification
An illumination problem: optimal apex and optimal orientation for a cone of light
Journal of Global Optimization
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We propose two different approaches for spherical separation of two sets. Both methods are based on minimizing appropriate nonconvex nondifferentiable error functions, which can be both expressed in a DC (Difference of two Convex) form. We tackle the problem by adopting the DC-Algorithm. Some numerical results on classical binary datasets are reported.