Multiple kernel learning of environmental data. case study: Analysis and mapping of wind fields

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5769 LNCS, Issue PART 2, 2009, Pages 933-943

  Foresti, Loris ^a   Tuia, Devis ^a   Pozdnoukhov, Alexei ^b   Kanevski, Mikhail ^a  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

Feature Selection; Multiple Kernel Learning; Support Vector Regression; Wind Speed Mapping

Indexed keywords

DATA-DRIVEN MODEL; DIRECTIONAL DERIVATIVE; EMPIRICAL STUDIES; ENVIRONMENTAL DATA; FEATURE SELECTION; FEATURE SUBSET; INTERPRETABILITY; MEAN WIND SPEED; MULTIPLE KERNEL LEARNING; OPTIMAL PARAMETER; REAL LIFE DATA; SPATIAL SCALE; SPATIAL VARIATIONS; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS; TERRAIN FEATURES; WIND FIELD; WIND SPEED;

BACKPROPAGATION; EDUCATION; LANDFORMS; MAPPING; PARAMETER ESTIMATION; REGRESSION ANALYSIS; SPEED; WIND EFFECTS;

NEURAL NETWORKS;

EID: 70450205603     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04277-5_94     Document Type: Conference Paper

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