- 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
- Copyright © Springer Science+Business Media New York
- Rights
- This metadata is freely available under a CCO license
- Subject
- Optimization; Nonsmooth optimization; Piecewise linear classifiers
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