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dc.rights.licenseUsers may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.
dc.contributorDrineas, Petros
dc.contributorMagdon-Ismail, Malik
dc.contributorZaki, Mohammed J., 1971-
dc.contributor.authorPaul, Saurabh
dc.date.accessioned2021-11-03T07:48:11Z
dc.date.available2021-11-03T07:48:11Z
dc.date.created2013-07-12T11:14:34Z
dc.date.issued2012-12
dc.identifier.urihttps://hdl.handle.net/20.500.13015/590
dc.descriptionDecember 2012
dc.descriptionSchool of Science
dc.description.abstractLet X ∈ R n×d be a data matrix of rank ρ, representing n points in R d . The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and minimum enclosing ball in the feature space are preserved to within -relative error, ensuring comparable generalization as in the original space. We present extensive experiments on synthetic and real-world datasets in support of the theory.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectComputer science
dc.titleRandom projections for support vector machines
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid116334
dc.digitool.pid116335
dc.digitool.pid116337
dc.digitool.pid116336
dc.digitool.pid116338
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Computer Science


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