We embed the concept of spherical separation of two disjoint finite sets of points into the semisupervised framework. This approach improves efficiency in the solution of real-world classification problems in which the number of unlabeled points is very large and labeling data is in general expensive. We develop a model characterized by an error function which is nonconvex and nondifferentiable, that we minimize by means of a bundle method. Numerical results on some small/large datasets drawn from literature are reported.
Semisupervised spherical separation
Annabella Astorino;
2015
Abstract
We embed the concept of spherical separation of two disjoint finite sets of points into the semisupervised framework. This approach improves efficiency in the solution of real-world classification problems in which the number of unlabeled points is very large and labeling data is in general expensive. We develop a model characterized by an error function which is nonconvex and nondifferentiable, that we minimize by means of a bundle method. Numerical results on some small/large datasets drawn from literature are reported.File in questo prodotto:
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