Nonsmooth Optimization Techniques for Semisupervised Classification

Author

Astorino, A. ; Fuduli, A.

Author_Institution

Univ. della Calabria, Rende

Volume

29

Issue

12

fYear

2007

Firstpage

2135

Lastpage

2142

Abstract

We apply nonsmooth optimization techniques to classification problems, with particular reference to the transductive support vector machine (TSVM) approach, where the considered decision function is nonconvex and nondifferentiable, hence 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.

Keywords

convex programming; learning (artificial intelligence); minimisation; pattern classification; support vector machines; TSVM approach; nonconvex decision function; nonsmooth convex minimization; nonsmooth optimization techniques; semisupervised classification; transductive support vector machine; Computational efficiency; Machine learning; Mathematical model; Optimization methods; Pattern classification; Predictive models; Semisupervised learning; Support vector machine classification; Support vector machines; Testing; bundle methods; nonsmooth optimization; semi--supervised learning; Algorithms; Artificial Intelligence; Computer Simulation; Models, Theoretical; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2007.1102

Filename

4359288