Nonlinear optimization and support vector machines

Veronica Piccialli () and Marco Sciandrone ()
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Veronica Piccialli: Dipartimento di Ingegneria Informatica, Automatica e Gestionale “A. Ruberti”, Sapienza Università degli Studi di Roma
Marco Sciandrone: Dipartimento di Ingegneria Informatica, Automatica e Gestionale “A. Ruberti”, Sapienza Università degli Studi di Roma

Annals of Operations Research, 2022, vol. 314, issue 1, No 3, 15-47

Abstract: Abstract Support vector machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in various fields. Nonlinear optimization plays a crucial role in SVM methodology, both in defining the machine learning models and in designing convergent and efficient algorithms for large-scale training problems. In this paper we present the convex programming problems underlying SVM focusing on supervised binary classification. We analyze the most important and used optimization methods for SVM training problems, and we discuss how the properties of these problems can be incorporated in designing useful algorithms.

Keywords: Statistical learning theory; Support vector machine; Convex quadratic programming; Wolfe’s dual theory; Kernel functions; Nonlinear optimization methods (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-022-04655-x

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