A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A D. C. Optimization Algorithm for Solving the Trust-Region Subproblem
SIAM Journal on Optimization
Solving a Class of Linearly Constrained Indefinite QuadraticProblems by D.C. Algorithms
Journal of Global Optimization
Neural Computation
Large-Scale Molecular Optimization from Distance Matrices by a D. C. Optimization Approach
SIAM Journal on Optimization
Minimizing Nonconvex Nonsmooth Functions via Cutting Planes and Proximity Control
SIAM Journal on Optimization
A new efficient algorithm based on DC programming and DCA for clustering
Journal of Global Optimization
Sparse eigen methods by D.C. programming
Proceedings of the 24th international conference on Machine learning
An algorithm for separating patterns by ellipsoids
IBM Journal of Research and Development
DC models for spherical separation
Journal of Global Optimization
New and efficient DCA based algorithms for minimum sum-of-squares clustering
Pattern Recognition
A class of semi-supervised support vector machines by DC programming
Advances in Data Analysis and Classification
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In this paper, we consider a binary supervised classification problem, called spherical separation, that consists of finding, in the input space or in the feature space, a minimal volume sphere separating the set $${\mathcal{A}}$$ from the set $${\mathcal{B}}$$ (i.e. a sphere enclosing all points of $${ \mathcal{A}}$$ and no points of $${\mathcal{B}}$$ ). The problem can be cast into the DC (Difference of Convex functions) programming framework and solved by DCA (DC Algorithm) as shown in the works of Astorino et al. (J Glob Optim 48(4):657---669, 2010). The aim of this paper is to investigate more attractive DCA based algorithms for this problem. We consider a new optimization model and propose two interesting DCA schemes. In the first scheme we have to solve a quadratic program at each iteration, while in the second one all calculations are explicit. Numerical simulations show the efficiency of our customized DCA with respect to the methods developed in Astorino et al.