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Experimental study and geometrical analysis of a linear programming support vector machine
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Ye, Jiankuan. Experimental study and geometrical analysis of a linear programming support vector machine. Retrieved from https://doi.org/doi:10.7282/T3Z60NPP

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TitleExperimental study and geometrical analysis of a linear
programming support vector machine
NameYe, Jiankuan (author); Kulikowski, Casimir (chair); Pavlovic, Vladimir (internal member); Elgammal, Ahmed (internal member); Pottenger, William (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2011
Other Date2011-01 (degree)
SubjectComputer Science, Linear programming--Data processing, Support vector machines, Quadratic programming--Computer programs, Machine learning
Extentxiv, 107 p. : ill.
DescriptionThis dissertation describes a systematic study of a linear programming SVM proposed by Vapnik, which directly minimizes the ratio of support vectors to the number of training samples in order to achieve best generalization ability. It mainly focuses on the properties of support vectors in linear programming (LP) SVM, including their numbers, class distribution and spatial location, as compared to the quadratic programming (QP) SVM. Our results show that the LP SVM achieves comparable performance to the QP SVM on all 50 synthetic data and 4 benchmark data. However, the LP SVM obtains the solution with many fewer support vectors. In all experiments with a linear kernel, the number of support vectors of the LP SVM never exceeds the number of dimensions. The locations of the support vectors of the LP SVM are more distant from the opposite class or the corresponding separating hyperplane than those for the QP SVM. In addition, the distribution of the support vectors between classes can be very biased in LP SVM. By relating the LP SVM to a heuristic two-stage optimization problem and a problem of representing the normal vector of the separating hyperplane by training samples, we carried out a geometrical analysis, and provide an explanation that supports the experi-mental results.
NotePh.D.
NoteIncludes bibliographical references
NoteIncludes vita
Noteby Jiankuan Ye
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3Z60NPP
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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