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Automated machine learning for supervised and unsupervised models with artificial neural networks
Descriptive
TitleInfo
Title
Automated machine learning for supervised and unsupervised models with artificial neural networks
Name
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Naghizadeh Nohadani
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Alireza
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Alireza Naghizadeh Nohadani
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author
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Metaxas
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Dimitris N
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Dimitris N Metaxas
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chair
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Abdeslam
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Abdeslam Boularias
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internal member
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Ahmed
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Ahmed Elgammal
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internal member
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Vivian Li
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Wei
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Wei Vivian Li
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Advisory Committee
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Rutgers University
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School of Graduate Studies
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Text
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theses
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ETD doctoral
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2021
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2021-01
Language
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English
Abstract
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Artificial Neural Networks (ANNs) are powerful machine learning tools to find and apply patterns for intelligent decision making. These tools can be combined with automation to select few results among many trials. Since ANNs are used for both supervised and unsupervised learning, automation can lead to more trusted learning methods across many fields and lead to exploring possibilities that are considered impossible with current technology. In this thesis, at first, I introduce a new form of ANN architecture which is used exclusively for automated robot navigation. By doing so, I provide a high-level overview of both computational neuroscience and the potential of automation. Next, I introduce Greedy AutoAugment to automate the learning of state-of-the-art neural networks for both big and small datasets. I also create an efficient model to evaluate clustering in unsupervised learning. The model is further expanded to introduce unsupervised learning for deep subspace clustering. In the end, I provide discussion and the future research plan for automating ANNs in machine learning applications.
Subject
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Topic
ANN
Subject
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Topic
Neural networks (Computer science)
Subject
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Topic
Computer Science
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Rutgers University Electronic Theses and Dissertations
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ETD_11444
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1 online resource (xi, 118 pages) : illustrations
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Ph.D.
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Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
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doi:10.7282/t3-8k6c-b717
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Rights
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The author owns the copyright to this work.
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Name
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Naghizadeh Nohadani
GivenName
Alireza
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2021-01-05 22:18:45
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Alireza Naghizadeh Nohadani
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Affiliation
Rutgers University. School of Graduate Studies
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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.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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2021-01-06T23:13:52
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2021-01-06T23:13:52
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