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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Published in | Computers & operations research Vol. 147; p. 105930 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.11.2022
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Subjects | |
Online Access | Get full text |
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