File(s) under permanent embargo
Piecewise linear classifiers based on nonsmooth optimization approaches
chapter
posted on 2014-01-01, 00:00 authored by A M Bagirov, R Kasimbeyli, G Öztürk, Julien UgonJulien UgonNonsmooth 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.
History
Title of book
Optimization in science and engineering : in honor of the 60th birthday of Panos M. PardalosChapter number
1Pagination
1 - 32Publisher
SpringerPlace of publication
New York, N.Y.Publisher DOI
ISBN-13
9781493908073ISBN-10
1493908073Language
engPublication classification
B1.1 Book chapterCopyright notice
2014, Springer Science+Business Media New YorkExtent
29Editor/Contributor(s)
T Rassias, C Floudas, S ButenkoUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC