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Towards scalable Bayesian nonparametric methods for data analytics

Huynh, Viet Huu 2017, Towards scalable Bayesian nonparametric methods for data analytics, PhD thesis, School of Information Technology, Deakin University.

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Title Towards scalable Bayesian nonparametric methods for data analytics
Author Huynh, Viet Huu
Institution Deakin University
School School of Information Technology
Faculty Faculty of Science, Engineering and Built Environment
Degree type Research doctorate
Degree name PhD
Thesis advisor Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Date submitted 2017-01
Keyword(s) big data
data mining
multi-level clustering
Summary Resorting big data to actionable information involves dealing with four dimensions of challenges in big data (called four V’s): volume, variety, velocity, veracity. In this study, we seek for novel Bayesian nonparametric models and scalable learning algorithms which can deal with these challenges of the big data era.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
010406 Stochastic Analysis and Modelling
170203 Knowledge Representation and Machine Learning
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
Description of original xix, 168, 4 pages : illustrations, tables, graphs, some coloured
Copyright notice ©The author.
Free to Read? Yes
Use Rights Creative Commons Attribution Share Alike licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30103238

Document type: Thesis
Collections: Higher degree theses (Open Access)
Open Access Collection
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Created: Fri, 13 Oct 2017, 11:07:37 EST by Asif, Yasmin

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.