Title :
Nonsmooth Optimization Techniques for Semisupervised Classification
Author :
Astorino, A. ; Fuduli, A.
Author_Institution :
Univ. della Calabria, Rende
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2007.1102
