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Learning tree-structured models from noisy data
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Nikolakakis, Konstantinos. Learning tree-structured models from noisy data. Retrieved from https://doi.org/doi:10.7282/t3-83y2-td17

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TitleLearning tree-structured models from noisy data
NameNikolakakis, Konstantinos (author); Sarwate, Anand D. (chair); El Rouayheb, Salim (internal member); Soljanin, Emina (internal member); Yates, Roy (internal member); Kalogerias, Dionysios S. (outside member); Rutgers University; School of Graduate Studies
Date Created2021
Other Date2021-05 (degree)
SubjectChow-Liu algorithm, Electrical and Computer Engineering
Extent1 online resource (xiii, 182 pages)
DescriptionGraphical models are a widely-used and powerful tool for analyzing high-dimensional structured data.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-83y2-td17
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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