Learning wind fields with multiple kernels

Learning wind fields with multiple kernels is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.1007/S00477-010-0405-0
P894zbMATH Open document ID1421.86019

P50authorMikhail KanevskiQ57412524
Devis TuiaQ58193230
P2093author name stringAlexei Pozdnoukhov
Loris Foresti
P2860cites workRobust Estimation of a Location ParameterQ22673907
A statistical framework for genomic data fusionQ30929198
Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structureQ31060397
Learning interpretable SVMs for biological sequence classificationQ33244587
On the mean accuracy of statistical pattern recognizersQ56431347
Gene Selection for Cancer Classification using Support Vector MachinesQ56535529
Data-driven topo-climatic mapping with machine learning methodsQ57527120
Multi-scale support vector algorithms for hot spot detection and modellingQ57527165
P433issue1
P6104maintained by WikiProjectWikiProject MathematicsQ8487137
P304page(s)51-66
P577publication date2010-06-03
P1433published inStochastic Environmental Research and Risk AssessmentQ15760514
P1476titleLearning wind fields with multiple kernels
P478volume25

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cites work (P2860)
Q57527042Active Learning for Monitoring Network Optimization
Q57527015Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
Q57527033Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

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