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2014 | OriginalPaper | Chapter

Robust Support Vector Machines with Polyhedral Uncertainty of the Input Data

Authors : Neng Fan, Elham Sadeghi, Panos M. Pardalos

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

In this paper, we use robust optimization models to formulate the support vector machines (SVMs) with polyhedral uncertainties of the input data points. The formulations in our models are nonlinear and we use Lagrange multipliers to give the first-order optimality conditions and reformulation methods to solve these problems. In addition, we have proposed the models for transductive SVMs with input uncertainties.

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Title: Robust Support Vector Machines with Polyhedral Uncertainty of the Input Data
Authors: Neng Fan
Elham Sadeghi
Panos M. Pardalos
Publisher: Springer International Publishing
Book: Learning and Intelligent Optimization
Print ISBN: 978-3-319-09583-7

Electronic ISBN: 978-3-319-09584-4

Copyright Year: 2014
DOI: https://doi.org/10.1007/978-3-319-09584-4_26

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