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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.

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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, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Date submitted 2019-01-21
Summary This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. The thesis develops efficient Bayesian optimization frameworks for hyperparameter tuning by utilizing different techniques like transfer learning, parallel computing, and domain-specific prior knowledge induction.
Language eng
Indigenous content off
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.
Copyright notice ┬ęThe author
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30117272

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Created: Thu, 31 Jan 2019, 07:55:16 EST by Bayne Christine

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.