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.

Nonlinear optimization and support vector machines / Piccialli, Veronica; Sciandrone, Marco*. - In: 4OR. - ISSN 1619-4500. - STAMPA. - 16:(2018), pp. 111-149. [10.1007/s10288-018-0378-2]

Nonlinear optimization and support vector machines

Sciandrone, Marco
2018

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.
2018
4OR
16
111
149
Piccialli, Veronica; Sciandrone, Marco*
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1139008
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