A Bayesian approach to joint feature selection and classifier design

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 26, Issue 9, 2004, Pages 1105-1111

  Krishnapuram, Balaji ^a   Hartemink, Alexander J ^b   Carin, Lawrence ^a   Figueiredo, Mário A T ^c  

^a Duke University   (United States)

^b Duke University   (United States)

^c IEEE   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC TARGET RECOGNITION; COMPUTATIONAL COMPLEXITY; LEARNING ALGORITHMS; MAXIMUM LIKELIHOOD ESTIMATION; NEURAL NETWORKS; OPTIMIZATION; POLYNOMIALS; REGRESSION ANALYSIS;

AUTOMATIC RELEVANCE DETERMINATION; EXPECTATION MAXIMIZATION ALGORITHM; FEATURE SELECTION; RELEVANCE VECTOR MACHINES; SPARSE PROBIT REGRESSION; STATISTICAL LEARNING; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

TUMOR MARKER;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COLON TUMOR; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; GENE EXPRESSION PROFILING; GENETICS; HUMAN; INFORMATION RETRIEVAL; LEUKEMIA; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; COLONIC NEOPLASMS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; GENE EXPRESSION PROFILING; HUMANS; INFORMATION STORAGE AND RETRIEVAL; LEUKEMIA; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 4344667429     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2004.55     Document Type: Article

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