A practical approach to model selection for support vector machines with a Gaussian kernel

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 41, Issue 2, 2011, Pages 330-340

  Varewyck, Matthias ^a   Martens, Jean Pierre ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Data mining; model selection; pattern classification; support vector machine (SVM)

Indexed keywords

ANALYTICAL FUNCTIONS; CENTRAL PROCESSING UNITS; COMPUTATIONAL EFFORT; COST PARAMETERS; DECISION FUNCTIONS; ERROR RATE; EXPERIMENTAL EVALUATION; FEATURE SPACE; GAUSSIAN KERNELS; GENERALIZATION ABILITY; KERNEL PARAMETER; LEARNING PARAMETERS; LEARNING PROCESS; MODEL SELECTION; MODEL SELECTION METHODS; PATTERN CLASSIFICATION;

DATA MINING; EQUIPMENT TESTING; GEARS; PROGRAM PROCESSORS; VECTORS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; DECISION SUPPORT SYSTEM; METHODOLOGY; NORMAL DISTRIBUTION; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DECISION SUPPORT TECHNIQUES; MODELS, STATISTICAL; MODELS, THEORETICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED;

EID: 79952898711     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2010.2053026     Document Type: Article

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