From Separating to Proximal Plane Classifiers: A Review

Maria Brigida Ferraro () and Mario Rosario Guarracino ()
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Maria Brigida Ferraro: Sapienza University of Rome, High Performance Computing and Networking Institute, National Research Council
Mario Rosario Guarracino: High Performance Computing and Networking Institute, National Research Council

A chapter in Clusters, Orders, and Trees: Methods and Applications, 2014, pp 167-180 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/978-1-4939-0742-7_10

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