Staff View
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 (type = personal)
NamePart (type = family)
Gupta
NamePart (type = given)
Mayank
NamePart (type = date)
1990-
DisplayForm
Mayank Gupta
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Kalantari
NamePart (type = given)
Bahman
DisplayForm
Bahman Kalantari
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Steiger
NamePart (type = given)
William
DisplayForm
William Steiger
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Awasthi
NamePart (type = given)
Pranjal
DisplayForm
Pranjal Awasthi
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2016
DateOther (qualifier = exact); (type = degree)
2016-05
Place
PlaceTerm (type = code)
xx
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2016
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
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
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7135
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (vii, 35 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Mayank Gupta
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3TT4T3M
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Gupta
GivenName
Mayank
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-04-07 20:56:25
AssociatedEntity
Name
Mayank Gupta
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
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
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
ApplicationName
pdfTeX-1.40.16
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-04-23T19:14:34
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-04-23T19:14:34
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2020