From Separating to Proximal Plane Classifiers: A Review

  • Maria Brigida Ferraro
  • Mario Rosario Guarracino
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 92)

Abstract

A review of parallel and proximal plane classifiers is proposed. We discuss separating plane classifier introduced in support vector machines and we describe different proposals to obtain two proximal planes representing the two classes in the binary classification case. In details, we deal with proximal SVM classification by means of a generalized eigenvalues problem. Furthermore, some regularization techniques are analyzed in order to solve the singularity of the matrices. For the same purpose, proximal support vector machine using local information is handled. In addition, a brief description of twin support vector machines and nonparallel plane proximal classifier is reported.

Keywords

Support vector machine Proximal plane classifier Regularized generalized eigenvalue classifier 

Notes

Acknowledgements

Authors would like to thank Dr. Panos Pardalos for the fruitful discussions and advice. This work has been partially funded by Italian Flagship project Interomics and by the Italian Ministry of Education, University and Research grant PON_00619.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Maria Brigida Ferraro
    • 1
  • Mario Rosario Guarracino
    • 2
  1. 1.Department of Statistical SciencesSapienza University of Rome, High Performance Computing and Networking Institute, National Research CouncilNaplesItaly
  2. 2.High Performance Computing and Networking Institute, National Research CouncilNaplesItaly

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