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
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 ┬ęThe author
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

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