Regularized extreme learning machine for regression with missing data

Neurocomputing

Volumn 102, Issue , 2013, Pages 45-51

  Yu, Qi ^a   Miche, Yoan ^a   Eirola, Emil ^a   van Heeswijk, Mark ^a   Séverin, Eric ^b   Lendasse, Amaury ^a,c,d  

^a AALTO UNIVERSITY   (Finland)

^b IESEG School of Management   (France)

^c BASQUE FOUNDATION FOR SCIENCE   (Spain)

^d UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

ELM; LARS; Missing data; Pairwise distance estimation; Ridge regression; Tikhonov regularization

Indexed keywords

ELM; LARS; MISSING DATA; PAIRWISE DISTANCES; RIDGE REGRESSION; TIKHONOV REGULARIZATION;

KNOWLEDGE ACQUISITION; MATRIX ALGEBRA; REGRESSION ANALYSIS;

LEARNING SYSTEMS;

ALGORITHM; ANALYTICAL PARAMETERS; ARTICLE; CONTROLLED STUDY; ERROR; EXPECTATION MAXIMIZATION ALGORITHM; FAST COMPUTATIONAL SPEED; GENERALIZATION PERFORMANCE; LEAST ANGLE REGRESSION REGULARIZED EXTREME LEARNING MACHINE; MACHINE LEARNING; MISSING DATA; MISSING DATA AT RANDOM; MISSING DATA COMPLETELY AT RANDOM; MISSING DATA NOT AT RANDOM; MONTE CARLO METHOD; PAIRWISE DISTANCE ESTIMATION; PRIORITY JOURNAL; REGRESSION ANALYSIS; RELIABILITY; STATISTICAL ANALYSIS; TIKHONOV REGULARIZED OPTIMALLY PRUNED EXTREME LEARNING MACHINE; TRAINING ERROR; VALIDATION PROCESS;

EID: 84870244730     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.02.040     Document Type: Article

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