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Towards area classification for large-scale fingerprint-based system
2016
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '16
In spacious and multi-area buildings, fingerprint-based localization often suffers from expensive location search. Besides, context knowledge like inside/outside-region and floor area is important for complete location service. To address above issues, beyond the algorithms finding the exact location point, we study accurate and efficient indoor area classification for large-scale fingerprint-based system. We first study leveraging the one-class classification to conduct inside/outside-region
doi:10.1145/2971648.2971689
dblp:conf/huc/HeTC16
fatcat:eu7yeevchfcovofki6dtgq67ae