Piecewise linear classifiers based on nonsmooth optimization approaches

Title: Piecewise linear classifiers based on nonsmooth optimization approaches
Creator: Bagirov, Adil; Kasimbeyli, Refail; Ozturk, Gurkan; Ugon, Julien
Date: 2014
Type: Text; Book chapter
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/155176
Identifier: vital:11230
Identifier: https://doi.org/10.1007/978-1-4939-0808-0_1
Identifier: ISBN:978-1-49390808-0
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.
Publisher: Springer New York
Relation: Optimization in Science and Engineering p. 1-32
Rights: This metadata is freely available under a CCO license
Subject: Optimization; Nonsmooth optimization; Piecewise linear classifiers
Reviewed

		Thumbnail	File	Description	Size	Format