Context- and cost-aware feature selection in ultra-low-power sensor interfaces

22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

Volumn , Issue , 2014, Pages 93-98

  Lauwereins, Steven ^a   Badami, Komail ^a   Meert, Wannes ^a   Verhelst, Marian ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COSTS; DECISION THEORY; FEATURE EXTRACTION; NEURAL NETWORKS; RECONFIGURABLE HARDWARE;

ADAPTIVE FEATURE EXTRACTIONS; C4.5 ALGORITHM; CONTEXT DEPENDENCES; EMBEDDED LEARNING; PROOF OF PRINCIPLES; RELEVANT FEATURES; ULTRA-LOW-POWER SENSORS; VOICE ACTIVITY DETECTORS;

LEARNING SYSTEMS;

EID: 84912542194     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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7. Siewioreck, D., et al., "SenSay: A context-Aware Mobile Phone.", page 248, Seventh IEEE International Symposium on Wearable Computers (ISWC'03),2003.
8. Jiang, Yifei, et al., "User-Centric Indoor Air Quality Monitoring on Mobile Devices." AI Magazine 34.2:11, 2013.