Ideal regularization for learning kernels from labels

Neural Networks

Volumn 56, Issue , 2014, Pages 22-34

  Pan, Binbin ^a,b   Lai, Jianhuang ^b   Shen, Lixin ^c  

^a SHENZHEN UNIVERSITY   (China)

^b SUN YAT SEN UNIVERSITY   (China)

^c SYRACUSE UNIVERSITY   (United States)

Author keywords

Ideal kernel; Kernel methods; Labels; Regularization; Semi supervised learning; Von Neumann divergence

Indexed keywords

ALGORITHMS; LABELING; SUPERVISED LEARNING;

IDEAL KERNEL; KERNEL METHODS; NEUMANN; REGULARIZATION; SEMI-SUPERVISED LEARNING;

MATRIX ALGEBRA;

ACCURACY; ALGORITHM; ARTICLE; CONTROLLED STUDY; GEOMETRY; INFORMATION PROCESSING; INTERMETHOD COMPARISON; KERNEL METHOD; LEARNING; LINEAR SYSTEM; MACHINE LEARNING; MATHEMATICAL PARAMETERS; PRIORITY JOURNAL; REGULARIZATION; SEMI SUPERVISED LEARNING; STANDARD; VON NEUMANN DIVERGENCE;

IDEAL KERNEL; KERNEL METHODS; LABELS; REGULARIZATION; SEMI-SUPERVISED LEARNING; VON NEUMANN DIVERGENCE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; HUMANS; LINEAR MODELS; NORMAL DISTRIBUTION; TIME;

EID: 84900001495     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2014.04.003     Document Type: Article

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