Multi-scale support vector algorithms for hot spot detection and modelling

Stochastic Environmental Research and Risk Assessment

Volumn 22, Issue 5, 2008, Pages 647-660

  Pozdnoukhov, Alexei ^a   Kanevski, Mikhail ^a  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

Author keywords

Kernel methods; Machine learning; Multi scale environmental modelling; Spatial mapping; Support vector regression

Indexed keywords

(PL) PROPERTIES; ALGORITHMIC APPROACH; DATA DRIVEN (DD); DATA MODELLING; DIFFERENT SCALES; EARLY WARNING SYSTEM (EWS); ENVIRONMENTAL DATA; ENVIRONMENTAL PROCESSES; HOT SPOT DETECTION; JOINT INFLUENCE; LARGE-SCALE MODELLING; MACHINE-LEARNING; MULTI SCALING; PREDICTIVE ABILITIES; PRIOR KNOWLEDGE; REAL-TIME MAPPING; SPATIAL DATA; SPATIAL SCALING; SUPPORT VECTOR ALGORITHMS; SUPPORT VECTOR REGRESSION (SVR);

ADMINISTRATIVE DATA PROCESSING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CESIUM; CONCENTRATION (PROCESS); CONFORMAL MAPPING; DECISION SUPPORT SYSTEMS; EVOLUTIONARY ALGORITHMS; FINANCIAL DATA PROCESSING; FOOD PROCESSING; INFORMATION MANAGEMENT; KNOWLEDGE MANAGEMENT; LEARNING ALGORITHMS; LEARNING SYSTEMS; MANAGEMENT INFORMATION SYSTEMS; NUMBER THEORY; NUMERICAL ANALYSIS; NUMERICAL METHODS; PAPER; SCALES (WEIGHING INSTRUMENTS); SEARCH ENGINES; VECTORS;

MATHEMATICAL MODELS;

ALGORITHM; DATA SET; HOT SPOT; MAPPING; MODELING; SPATIAL ANALYSIS;

EID: 45749110247     PISSN: 14363240     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00477-007-0162-x     Document Type: Article

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