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Ensemble Supervised and Unsupervised Learning With Kepler Variable Stars

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dc.contributor.advisor Borne, Kirk
dc.contributor.author Bass, Gideon Perry
dc.creator Bass, Gideon Perry
dc.date.accessioned 2016-04-19T19:28:48Z
dc.date.available 2016-04-19T19:28:48Z
dc.date.issued 2015
dc.identifier.uri https://hdl.handle.net/1920/10167
dc.description.abstract Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. I present a study on variable stars in the Kepler field using these techniques, and the novel work of unsupervised learning. I use new methods of characterization and multiple independent classifiers to produce an ensemble classifier that equals or matches existing classification abilities. I also explore the possibilities of unsupervised learning in making novel feature discovery in stars.
dc.format.extent 120 pages
dc.language.iso en
dc.rights Copyright 2015 Gideon Perry Bass
dc.subject Astrophysics en_US
dc.subject Astronomy en_US
dc.subject Data Mining en_US
dc.subject Machine Learning en_US
dc.subject Variable Stars en_US
dc.title Ensemble Supervised and Unsupervised Learning With Kepler Variable Stars
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Physics en
thesis.degree.grantor George Mason University en


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