Home
Resource
Classifiers of massive and structured data problems: algorithms and applications
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(3.58 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Balakrishnan, Suhrid. Classifiers of massive and structured data problems: algorithms and applications. Retrieved from https://doi.org/doi:10.7282/T3959J0N

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

Uniform TitleClassifiers of massive and structured data problems: algorithms and applications
NameBalakrishnan, Suhrid (author); Madigan, David (chair); Pavlovic, Vladimir (internal member); Kulikowski, Casimir (internal member); Blei, David (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2007
Other Date2007 (degree)
SubjectComputer Science, Data mining, Machine learning, Artificial intelligence
Extentx, 107 pages
DescriptionThe 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.
NotePh.D.
NoteIncludes bibliographical references.
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3959J0N
LanguageEnglish
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024