Bayesian nonparametric learning of contexts and activities from pervasive signals
Contribuinte(s) |
Phung, Dinh Venkatesh, Svetha |
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Data(s) |
01/09/2015
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Deakin University, Faculty of Science Engineering and Built Environment, School of Information Technology |
Relação |
http://dro.deakin.edu.au/eserv/DU:30079712/nguyen-agreement-2015.pdf http://dro.deakin.edu.au/eserv/DU:30079712/nguyen-bayesiannoparametric-2015A.pdf |
Direitos |
The author |
Palavras-Chave | #pervasive signals #data mining #machine learning #pattern recognition |
Tipo |
Thesis |