Abstract
Generalized eigenvalue proximal support vector machine (GEPSVM) is the first nonparallel support vector machine. Compared to standard support vector machine (SVM), GEPSVM coped with the “Xor” problem well. In this paper, by defining a generalized elastic net regularization which is the combination of the L2-norm and Lq-norm, we propose a generalized elastic net Lp-norm nonparallel proximal support vector machine (GLpNPSVM), where p,q>0. GLpNPSVM measures the distance of a sample to each hyperplane by the Lp-norm, and hence can achieve desired performance by choosing appropriate p. In addition, the generalized elastic net regularization makes GLpNPSVM own good generalization ability. GLpNPSVM is a generalized formulation, and GEPSVM and some of its improvements are special cases of GLpNPSVM. A simple but effective iterative technique is introduced to solve GLpNPSVM, and we prove its convergence for certain p,q>0. Experimental results on different types of contaminated data sets show the effectiveness of GLpNPSVM.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering Applications of Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
