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
Making large-scale support vector machine learning practical
Advances in kernel methods
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
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
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Nonsmooth Optimization Techniques for Semisupervised Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new maximal-margin spherical-structured multi-class support vector machine
Applied Intelligence
DC models for spherical separation
Journal of Global Optimization
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Pattern classification via single spheres
DS'05 Proceedings of the 8th international conference on Discovery Science
An illumination problem: optimal apex and optimal orientation for a cone of light
Journal of Global Optimization
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We face the problem of strictly separating two sets of points by means of a sphere, considering the two cases where the center of the sphere is fixed or free, respectively. In particular, for the former we present a fast and simple solution algorithm, whereas for the latter one we use the DC-Algorithm based on a DC decomposition of the error function. Numerical results for both the cases are presented on several classical binary datasets drawn from the literature.