title: Nonsmooth optimization techniques for semisupervised classification.
abstract: We apply nonsmooth optimization techniques to classification problems, with particular reference to the TSVM (Transductive Support Vector Machine) approach, where the considered decision function is nonconvex and nondifferentiable and then difficult to minimize. We present some numerical results obtained by running the proposed method on some standard test problems drawn from the binary classification literature.
authors.affiliation: Istituto di Calcolo e Reti ad Alte Prestazioni C.N.R., Dipartimento di Elettronica Informatica e Sistemistica, Università della Calabria, Italy. astorino@icar.cnr.it
authors.forename: Annabella
authors.initials: A
authors.lastname: Astorino
authors.forename: Antonio
authors.initials: A
authors.lastname: Fuduli
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity