Multiple kernel learning for heterogeneous anomaly detection: Algorithm and aviation safety case study

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2010, Pages 47-55

  Das, Santanu ^a   Matthews, Bryan L ^a   Srivastava, Ashok N ^a   Oza, Nikunj C ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

Aeronautics; Anomaly detection; Prediction; Prognostics

Indexed keywords

AERONAUTICS; ANOMALY DETECTION; APPLICATION DOMAINS; AVIATION INDUSTRY; AVIATION SAFETY; AVIATION SYSTEMS; COMMERCIAL AIRCRAFT; COMPLEX DYNAMICAL SYSTEMS; CONTINUOUS DATA; CONTINUOUS MEASUREMENTS; FLIGHT PARAMETERS; GUIDANCE , NAVIGATION , AND CONTROLS; HIGH DIMENSIONAL DATA; MULTIPLE KERNEL LEARNING; NOMINAL SYSTEM; NOVEL ALGORITHM; PREDICTION; PROGNOSTICS; PROPULSION SYSTEM; RAPID RATE; REAL WORLD DATA; STATE-OF-THE-ART METHODS; VERY LARGE DATUM; WORLD-WIDE OPERATIONS;

AERODYNAMICS; AIR NAVIGATION; AIRCRAFT ACCIDENTS; AIRCRAFT CONTROL; CLUSTERING ALGORITHMS; DATA COMMUNICATION SYSTEMS; DATA MINING; DYNAMICAL SYSTEMS; LARGE SCALE SYSTEMS; PROPULSION;

FLIGHT CONTROL SYSTEMS;

EID: 77956210503     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835813     Document Type: Conference Paper

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