Polyhedral separation via difference of convex (DC) programming-Reference-Cited by-同舟云学术

Polyhedral separation via difference of convex (DC) programming

Published:2021-04-07 Issue:19 Volume:25 Page:12605-12613
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Astorino Annabella,Francesco Massimo Di^ORCID,Gaudioso Manlio,Gorgone Enrico,Manca Benedetto

Abstract

AbstractWe consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the results of the implementation of DCA on a number of benchmark classification datasets.

Funder

Università degli Studi di Cagliari

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-021-05758-6.pdf

Reference31 articles.

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2. Astorino A, Fuduli A (2015) Support vector machine polyhedral separability in semisupervised learning. J Optim Theory Appl 164(3):1039–1050

3. Astorino A, Fuduli A, Gaudioso M (2010) DC models for spherical separation. J Global Optim 48(4):657–669

4. Astorino A, Fuduli A, Gaudioso M (2012) Margin maximization in spherical separation. Comput Optim Appl 53(2):301–322

5. Astorino A, Gaudioso M (2002) Polyhedral separability through successive LP. J Optim Theory Appl 112(2):265–293

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