Experimentally optimal ν in support vector regression for different noise models and parameter settings

Neural Networks

Volumn 17, Issue 1, 2004, Pages 127-141

  Chalimourda, Athanassia ^a   Schölkopf, Bernhard ^b   Smola, Alex J ^c  

^a RUHR UNIVERSITY BOCHUM   (Germany)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Gaussian kernel; Model selection; Optimal ; Risk minimization; Support Vector machine parameters; Support Vector machines; Support Vector regression; Support Vector machines

Indexed keywords

ERROR ANALYSIS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; REGRESSION ANALYSIS; SPURIOUS SIGNAL NOISE; VECTORS;

SUPPORT VECTOR MACHINES (SVM);

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; EXPERIMENT; LEARNING; NOISE; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; PARAMETER; PREDICTION; PRIORITY JOURNAL; REGRESSION ANALYSIS; THEORETICAL MODEL;

EID: 0346881149     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(03)00209-0     Document Type: Article

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