@inproceedings{af8e27335f0b46b5b464e586c7c47139,
title = "Robust support vector machines with polyhedral uncertainty of the input data",
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.",
keywords = "Classification, Nonlinear programming, Polyhedral uncertainty, Robust optimization, Support vector machines",
author = "Neng Fan and Elham Sadeghi and Pardalos, {Panos M.}",
year = "2014",
doi = "10.1007/978-3-319-09584-4_26",
language = "English (US)",
isbn = "9783319095837",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "291--305",
booktitle = "Learning and Intelligent Optimization - 8th International Conference, Lion 8, Revised Selected Papers",
note = "8th International Conference on Learning and Intelligent OptimizatioN, LION 2014 ; Conference date: 16-02-2014 Through 21-02-2014",
}