Theoretical bounds of majority voting performance for a binary classification problem

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 27, Issue 12, 2005, Pages 1988-1995

  Narasimhamurthy, Anand ^a  

^a The Pennsylvania State University   (United States)

Author keywords

Classifier design and evaluation; Classifier diversity measures; Majority voting; Theoretical bounds

Indexed keywords

LINEAR PROGRAMMING; MAJORITY LOGIC; MATRIX ALGEBRA; OPTIMIZATION; PROBABILITY; VECTORS;

BINARY CLASSIFICATION; CLASSIFIER DIVERSITY MEASURES; MAJORITY VOTING; THEORETICAL BOUNDS;

PATTERN RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 30144441888     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2005.249     Document Type: Article

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