Robust and Distributionally Robust Optimization Models for Linear Support Vector Machine

In this paper we present novel data-driven optimization models for Support Vector Machines (SVM), with the aim of linearly separating two sets of points that have non-disjoint convex closures. Traditional classification algorithms assume that the training data points are always known exactly. Howeve...

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Bibliographic Details
Published inComputers & operations research Vol. 147; p. 105930
Main Authors Faccini, Daniel, Maggioni, Francesca, Potra, Florian A.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.11.2022
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