An energy-efficient kernel framework for large-scale data modeling and classification

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum

Volumn , Issue , 2011, Pages 404-408

  Yoo, Paul D ^a   Ng, Jason W P ^a   Zomaya, Albert Y ^b  

^a KHALIFA UNIVERSITY   (United Arab Emirates)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Energy efficient computing; Large scale data; Machine learning

Indexed keywords

COMPUTATIONAL COSTS; DATA CENTER OPERATIONS; DATA MODELING; DATA SETS; ENERGY EFFICIENT; ENERGY WASTES; ENERGY-DRIVEN; ENERGY-RELATED COSTS; GLOBALOPTIMUM; LARGE-SCALE DATA; MACHINE-LEARNING; MARKET NEEDS; MODEL FREE; NON-PARAMETRIC; OPERATIONAL EXPENSE; PERFORMANCE-DRIVEN; TEST ERRORS;

CLASSIFICATION (OF INFORMATION); COSTS; DISTRIBUTED COMPUTER SYSTEMS; DISTRIBUTED PARAMETER NETWORKS; ENERGY CONVERSION; ENVIRONMENTAL IMPACT; LEARNING SYSTEMS; MODELS; PARALLEL PROCESSING SYSTEMS;

ENERGY EFFICIENCY;

EID: 83455249953     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2011.178     Document Type: Conference Paper

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