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A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem
Descriptive
TitleInfo
Title
A comparison of the triangle algorithm and sequential minimal optimization algorithm for solving the hard margin problem
Name
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Gupta
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Mayank
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1990-
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Mayank Gupta
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author
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Kalantari
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Bahman
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Bahman Kalantari
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Advisory Committee
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chair
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Steiger
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William
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William Steiger
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Advisory Committee
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internal member
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(type = personal)
NamePart
(type = family)
Awasthi
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Pranjal
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Pranjal Awasthi
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Advisory Committee
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internal member
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
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Text
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theses
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2016
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2016-05
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xx
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2016
Language
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eng
Abstract
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In this article we consider the problem of testing, for two nite sets of points in the Euclidean space, if their convex hulls are disjoint and computing an optimal supporting hyperplane if so. This is a fundamental problem of classi cation in machine learning known as the hard-margin SVM. The problem can be formulated as a quadratic programming problem. The SMO algorithm [1] is the current state of art algorithm for solving it, but it does not answer the question of separability. An alternative to solving both problems is the triangle algorithm [2], a geometrically inspired algorithm, initially described for the convex hull membership problem [3], a fundamental problem in linear programming. First, we describe the experimental performance of the triangle algorithm for testing the intersection of two convex hulls. Next, we compare the performance of triangle algorithm with SMO for nding the optimal supporting hyperplane. Based on experimental results ranging up to 5000 points in each set in dimensions up to 10000, the triangle algorithm outperforms SMO.
Subject
(authority = RUETD)
Topic
Computer Science
Subject
(authority = ETD-LCSH)
Topic
Convex sets
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Rutgers University Electronic Theses and Dissertations
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ETD_7135
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (vii, 35 p. : ill.)
Note
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M.S.
Note
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Includes bibliographical references
Note
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by Mayank Gupta
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
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doi:10.7282/T3TT4T3M
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(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Gupta
GivenName
Mayank
Role
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Type
Permission or license
DateTime
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2016-04-07 20:56:25
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Mayank Gupta
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Affiliation
Rutgers University. Graduate School - New Brunswick
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Type
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
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ETD
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windows xp
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2016-04-23T19:14:34
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2016-04-23T19:14:34
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