Efficient hyperparameter tuning using Bayesian optimization Theckel Joy, Tinu 2019, Efficient hyperparameter tuning using Bayesian optimization, Ph.D. thesis, School of Information Technology, Deakin University. Attached Files Name Description MIMEType Size Downloads Title Efficient hyperparameter tuning using Bayesian optimization Author Theckel Joy, Tinu Institution Deakin University School School of Information Technology Faculty Faculty of Science, Engineering and Built Environment Degree type Research doctorate Degree name Ph.D. Thesis advisor Rana, Santu orcid.org/0000-0003-2247-850X Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930 Date submitted 2019-01-21 Summary This thesis focuses on the problem of selecting optimal hyperparameters of machine learning algorithms. The study promises the development of efficient Bayesian optimization methods while addressing different facets of the problem. The proposed methods address the drawbacks of the existing methods and can be applied for various hyperparameter tuning tasks. Language eng Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences Description of original 149 p. Restricted until 2020-01-21 Copyright notice ©The author Persistent URL http://hdl.handle.net/10536/DRO/DU:30117272 Document type: Thesis Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Higher degree theses (citation only)   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 31 Jan 2019, 07:55:34 EST Fri, 01 Feb 2019, 07:04:48 EST Thu, 14 Mar 2019, 14:01:29 EST Thu, 14 Mar 2019, 14:02:32 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 58 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Thu, 31 Jan 2019, 07:55:16 EST by Bayne Christine

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