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Piecewise Linear Classifiers Based on Nonsmooth Optimization Approaches

In: Optimization in Science and Engineering

Author

Listed:
  • 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)

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.

Suggested Citation

  • Adil M. Bagirov & Refail Kasimbeyli & Gürkan Öztürk & Julien Ugon, 2014. "Piecewise Linear Classifiers Based on Nonsmooth Optimization Approaches," Springer Books, in: Themistocles M. Rassias & Christodoulos A. Floudas & Sergiy Butenko (ed.), Optimization in Science and Engineering, edition 127, pages 1-32, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4939-0808-0_1
    DOI: 10.1007/978-1-4939-0808-0_1
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