Kernel methods and the exponential family

Neurocomputing

Volumn 69, Issue 7-9 SPEC. ISS., 2006, Pages 714-720

  Canu, Stéphane ^a,d   Smola, Alex ^b,c  

^a UNIVERSITÉ ET INSA DE ROUEN   (France)

^b CSIRO   (Australia)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^d 1 PSI FRE CNRS 2645   (France)

Author keywords

Exponential families; Kernel methods; Novelty detection

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; MATHEMATICAL MODELS; SET THEORY; STATISTICAL METHODS;

EXPONENTIAL FAMILIES; KERNEL METHODS; NOVELTY DETECTION; REPRODUCING KERNEL HILBERT SPACE; SUPPORT VECTOR MACHINE;

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; INFORMATION PROCESSING; INFORMATION PROCESSING DEVICE; LEARNING; LOGISTIC REGRESSION ANALYSIS; MATHEMATICAL COMPUTING; MAXIMUM LIKELIHOOD METHOD; NORMAL DISTRIBUTION; PRIORITY JOURNAL; SEQUENTIAL ANALYSIS; STATISTICAL MODEL;

EID: 32544437023     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2005.12.009     Document Type: Article

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