Supervised and semi-supervised online boosting tree for industrial machine vision application

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2011, Pages 43-51

  Wang, Fan ^a   Xu, Xinyu ^b   Yuan, Chang ^b   Van Beek, Peter ^b  

^a Stanford University   (United States)

^b SHARP LABORATORIES OF AMERICA   (United States)

Author keywords

Boosting tree; Defect classification; Defect detection; Online learning

Indexed keywords

BATCH MODES; BOOSTING TREE; CLASSIFICATION ACCURACY; CO-TRAINING; DATA PROPERTIES; DEFECT CLASSIFICATION; DEFECT DETECTION; HUMAN OPERATOR; INDUSTRIAL INSPECTIONS; INDUSTRIAL MACHINE VISION; KNOWLEDGE DISCOVERY AND DATA MININGS; LIMITED MEMORY; MACHINE LEARNING TECHNIQUES; ON-LINE BOOSTING; ON-LINE FASHION; ONLINE LEARNING; ONLINE PREDICTION; SEMI-SUPERVISED; TRAINING SAMPLE; UNLABELED SAMPLES;

ADAPTIVE BOOSTING; COMPUTATIONAL COMPLEXITY; COMPUTER VISION; DATA MINING; INDUSTRY; INSPECTION; KNOWLEDGE REPRESENTATION; PERSONNEL TRAINING; PLANT EXTRACTS; SAMPLING; SENSORS; TREES (MATHEMATICS);

E-LEARNING;

EID: 80051697215     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2003653.2003658     Document Type: Conference Paper

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