Piecewise Linear Classifiers Based on Nonsmooth Optimization Approaches

Adil M. Bagirov (), Refail Kasimbeyli (), Gürkan Öztürk () and Julien Ugon ()
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Adil M. Bagirov: Federation University, Australia, School of Science, Information Technology and Engineering
Refail Kasimbeyli: Anadolu University, Department of Industrial Engineering
Gürkan Öztürk: Anadolu University, Department of Industrial Engineering
Julien Ugon: Federation University, Australia, School of Science, Information Technology and Engineering

A chapter in Optimization in Science and Engineering, 2014, pp 1-32 from Springer

Abstract: Abstract Nonsmooth optimization provides efficient algorithms for solving many machine learning problems. In particular, nonsmooth optimization approaches to supervised data classification problems lead to the design of very efficient algorithms for their solution. In this chapter, we demonstrate how nonsmooth optimization algorithms can be applied to design efficient piecewise linear classifiers for supervised data classification problems. Such classifiers are developed using a max–min and a polyhedral conic separabilities as well as an incremental approach. We report results of numerical experiments and compare the piecewise linear classifiers with a number of other mainstream classifiers.

Keywords: Piecewise Linear Function; Nonsmooth Optimization; Piecewise Linear Boundary; Mainstream Classifier; Data Classification Problem (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4939-0808-0_1

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DOI: 10.1007/978-1-4939-0808-0_1

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