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Classifiers of massive and structured data problems: algorithms and applications
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Title
Classifiers of massive and structured data problems: algorithms and applications
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Balakrishnan
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Suhrid
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Suhrid Balakrishnan
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author
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Madigan
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David
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Advisory Committee
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David Madigan
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chair
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Pavlovic
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Vladimir
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Advisory Committee
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Vladimir Pavlovic
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internal member
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Kulikowski
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Casimir
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Advisory Committee
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Casimir Kulikowski
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internal member
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Blei
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David
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Advisory Committee
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David M Blei
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007
Language
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English
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electronic
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x, 107 pages
Abstract
The last two decades have seen the emergence of vast and unprecedented data repositories. Extraordinary opportunities now present themselves for new data analysis methods that can harness these repositories. As larger and larger amounts of widely varying types of data are constantly being collected and assimilated, the
task of making use of such data opens up interesting and challenging avenues of research.
This thesis deals with specific problems in data mining and machine learning in this setting. In particular we describe algorithms and applications for classification problems where
computational restrictions become limiting (resource bounded algorithms and online/streaming algorithms) as well as models and algorithms for certain problems where the structure of the input is leveraged to provide not only accurate, but also interpretable classifiers.
task of making use of such data opens up interesting and challenging avenues of research.
This thesis deals with specific problems in data mining and machine learning in this setting. In particular we describe algorithms and applications for classification problems where
computational restrictions become limiting (resource bounded algorithms and online/streaming algorithms) as well as models and algorithms for certain problems where the structure of the input is leveraged to provide not only accurate, but also interpretable classifiers.
Note
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Ph.D.
Note
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Includes bibliographical references.
Subject
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Topic
Computer Science
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Topic
Data mining
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Topic
Machine learning
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Topic
Artificial intelligence
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Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.15772
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ETD_368
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NjNbRU
Identifier
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doi:10.7282/T3959J0N
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ETD doctoral
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Rights
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Copyright protected
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Open
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Name
Suhrid Balakrishnan
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Affiliation
Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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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.
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