Unsupervised labelling of sequential data for
location identification in indoor environments

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Unsupervised labelling of sequential data for location identification in indoor environments

In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we for...

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Bibliographic Details
Main Authors:	Pérez López, Iker, Pinchin, James, Brown, Michael, Blum, Jesse, Sharples, Sarah
Format:	Article
Published:	Elsevier 2016
Subjects:	Unsupervised Labelling; Sequential Data; Indoor Positioning; Ubiquitous Computing; Graphical Models
Online Access:	https://eprints.nottingham.ac.uk/33787/

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