Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency

Neurocomputing

Volumn 169, Issue , 2015, Pages 225-235

  Oneto, Luca ^a   Ghio, Alessandro ^a   Ridella, Sandro ^a   Anguita, Davide ^b  

^a UNIVERSITY OF GENOA   (Italy)

^b UNIVERSITY OF GENOA   (Italy)

Author keywords

Energy efficiency; Local Rademacher Complexity; Mobile devices; Performance Assessment; Supervised Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; ELECTRIC BATTERIES; LEARNING ALGORITHMS; LEARNING SYSTEMS; MOBILE DEVICES; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; SMARTPHONES; SUPERVISED LEARNING;

COMMUNICATION CAPABILITIES; GENERALIZATION ABILITY; HUMAN ACTIVITY RECOGNITION; LOW COMPUTATIONAL COMPLEXITY; PERFORMANCE ASSESSMENT; RADEMACHER COMPLEXITY; RESOURCE-LIMITED DEVICES; STATISTICAL LEARNING THEORY;

ENERGY EFFICIENCY;

ARTICLE; CALCULATION; ELECTRIC CAPACITANCE; ENERGY; ENERGY EFFICIENCY; ENERGY RESOURCE; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MOBILE PHONE; POWER SUPPLY; PRIORITY JOURNAL; PROCESS DEVELOPMENT;

EID: 84938211160     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.12.099     Document Type: Article

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