title: A geometric nearest point algorithm for the efficient solution of the SVM classification task.
abstract: Geometric methods are very intuitive and provide a theoretically solid approach to many optimization problems. One such optimization task is the support vector machine (SVM) classification, which has been the focus of intense theoretical as well as application-oriented research in machine learning. In this letter, the incorporation of recent results in reduced convex hulls (RCHs) to a nearest point algorithm (NPA) leads to an elegant and efficient solution to the SVM classification task, with encouraging practical results to real-world classification problems, i.e., linear or nonlinear and separable or nonseparable.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Mavroforakis
authors.forename: Margaritis
authors.initials: M
authors.lastname: Sdralis
authors.forename: Sergios
authors.initials: S
authors.lastname: Theodoridis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*