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Making sense of pervasive signals: a machine learning approach

Nguyen, Thanh Binh 2018, Making sense of pervasive signals: a machine learning approach, Ph.D thesis, School of Information Technology, Deakin University.

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Title Making sense of pervasive signals: a machine learning approach
Author Nguyen, Thanh BinhORCID iD for Nguyen, Thanh Binh orcid.org/0000-0003-4527-8826
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 Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Date submitted 2018-05-29
Summary This study focused on challenges come from noisy and complex pervasive data. We proposed new Bayesian nonparametric models to infer co-patterns from multi-channel data collected from pervasive devices. By making sense of pervasive data, the study contributes to the development of Machine Learning and Data Mining in Big Data era.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080504 Ubiquitous Computing
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
Description of original 144 p.
Copyright notice ┬ęThe author
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30110919

Document type: Thesis
Collections: Higher degree theses (Open Access)
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Created: Wed, 11 Jul 2018, 14:58:32 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.