A meta-learning approach to the regularized learning-Case study: Blood glucose prediction

Neural Networks

Volumn 33, Issue , 2012, Pages 181-193

  Naumova, V ^a   Pereverzyev, S V ^a   Sivananthan, S ^a  

^a JOHANN RADON INSTITUTE FOR COMPUTATIONAL AND APPLIED MATHEMATICS   (Austria)

Author keywords

Adaptive parameter choice; Blood glucose prediction; Kernel choice; Learning theory; Meta learning; Regularization

Indexed keywords

ADAPTIVE PARAMETERS; BLOOD GLUCOSE; KERNEL CHOICE; LEARNING THEORY; METALEARNING; REGULARIZATION;

BLOOD; FORECASTING; LEARNING ALGORITHMS; PARAMETERIZATION;

GLUCOSE;

GLUCOSE;

ADULT; AGED; ARTICLE; CASE STUDY; CONTROLLED STUDY; DIABETIC PATIENT; FEMALE; GLUCOSE BLOOD LEVEL; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; KERNEL METHOD; LEARNING ALGORITHM; MALE; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; MEDICAL LITERATURE; NON INSULIN DEPENDENT DIABETES MELLITUS; PATIENT INFORMATION; PREDICTION; PRIORITY JOURNAL; PROBLEM SOLVING; REGULARIZATION PARAMETER; STATISTICAL PARAMETERS;

ADOLESCENT; ADULT; AGED; ALGORITHMS; BLOOD GLUCOSE; CASE-CONTROL STUDIES; DIABETES MELLITUS; FEMALE; HUMANS; LEARNING; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; YOUNG ADULT;

EID: 84863859637     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.05.004     Document Type: Article

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