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Bayesian nonparametric learning of contexts and activities from pervasive signals

Nguyen, Thuong Cong 2015, Bayesian nonparametric learning of contexts and activities from pervasive signals, Ph.D. thesis, School of Information Technology, Deakin University.

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Title Bayesian nonparametric learning of contexts and activities from pervasive signals
Author Nguyen, Thuong Cong
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, Dinh
Venkatesh, Svetha
Date submitted 2015-09
Keyword(s) pervasive signals
data mining
machine learning
pattern recognition
Summary This thesis develops machine learning techniques to discover activities and contexts from pervasive sensor data. These techniques are especially suitable for streaming sensor data as they can infer the context space automatically. They are applicable in many real world applications such as activity monitoring or organization management.
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 xiii,193 p.
Copyright notice ┬ęThe author
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079712

Document type: Thesis
Collection: Higher degree theses (citation only)
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Created: Fri, 20 Nov 2015, 14:24:52 EST by Prue Plummer

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