Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1703168 | Applied Mathematical Modelling | 2015 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Annabella Astorino, Antonio Fuduli,