Semi-supervised Deep Learning-based Methods for Indoor Outdoor Detection

Illyyne Saffar (1, 2, 3) , Marie-Line Alberi-Morel (1) , Kamal Deep Singh (4, 5) , César Viho (2, 3)

1 Nokia Bell Labs [Nozay]
2 UR - Université de Rennes
3 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
4 LHC - Laboratoire Hubert Curien
5 UJM - Université Jean Monnet - Saint-Étienne

Mots clés: web mining and graph mining; Big data practices and applications; Mobility and big data; crowdsourcing; data mining; Machine learning; Semi-Supervised Learning; 3GPP radio measurement; Deep Learning; Environment Classification; real user activity; Indoor Outdoor Detection

Domaines: Intelligence artificielle [cs.AI]; Réseaux et télécommunications [cs.NI]

Illyyne Saffar, Marie-Line Alberi-Morel, Kamal Deep Singh, César Viho. Semi-supervised Deep Learning-based Methods for Indoor Outdoor Detection. ICC 2019 - IEEE International Conference on Communications, May 2019, Shangai, China. pp.1-7, ⟨10.1109/ICC.2019.8761297⟩. ⟨hal-02011449⟩