A Bayesian approach to support vector machines for the binary classification

Neurocomputing

Volumn 72, Issue 1-3, 2008, Pages 177-185

  Yu, Jiangsheng ^a   Cheng, Fei ^b   Xiong, Huilin ^c   Qu, Wanling ^a   Chen, Xue wen ^d  

^a PEKING UNIVERSITY   (China)

^b BEIJING JIAOTONG UNIVERSITY   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

^d UNIVERSITY OF KANSAS   (United States)

Author keywords

Bayesian method; Classification; Feature space; Gaussian mixture model; Kernel; Reversible jump Markov chain Monte Carlo (RJMCMC); Support vector machine (SVM)

Indexed keywords

BAYESIAN NETWORKS; GEARS; IMAGE RETRIEVAL; LEARNING SYSTEMS; MARKOV PROCESSES; MONTE CARLO METHODS; MULTILAYER NEURAL NETWORKS; UNCERTAIN SYSTEMS; VECTORS;

BAYESIAN METHOD; CLASSIFICATION; FEATURE SPACE; GAUSSIAN MIXTURE MODEL; KERNEL; REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO (RJMCMC); SUPPORT VECTOR MACHINE (SVM);

SUPPORT VECTOR MACHINES;

ANALYTICAL ERROR; ARTICLE; BAYES THEOREM; CANCER CLASSIFICATION; CONTROLLED STUDY; DECISION MAKING; EXPERIMENTAL DESIGN; FEMALE; HIGH THROUGHPUT SCREENING; HUMAN; MAJOR CLINICAL STUDY; MALE; MONTE CARLO METHOD; OVARY CANCER; PRIORITY JOURNAL; PROBABILITY; PROSTATE CANCER; RANDOM SAMPLE; STATISTICAL DISTRIBUTION; STATISTICAL PARAMETERS; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 55949127886     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.06.010     Document Type: Article

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