Predicting classifier performance with a small training set: Applications to computer-aided diagnosis and prognosis

2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Volumn , Issue , 2010, Pages 229-232

  Basavanhally, Ajay ^a   Doyle, Scott ^a   Madabhushi, Anant ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AD HOC PROCESS; ANNOTATED TRAINING DATA; APRIORI; BREAST CANCER; CLASSIFIER PERFORMANCE; CLASSIFIER TRAINING; CLINICAL TRIAL; DATA SETS; EMPIRICAL RESULTS; K-NEAREST NEIGHBORS; LARGE AMOUNTS OF DATA; LOW GRADE; LOW LEVEL; PIXEL LEVEL; POWER LAW MODEL; SMALL DATA; SMALL TRAINING; STATISTICAL LEARNING; TRAINING DATA;

CLASSIFIERS; COMPUTER AIDED DESIGN; COMPUTER AIDED DIAGNOSIS; DECISION TREES; FORECASTING; FUZZY CONTROL; MACROS; MAMMOGRAPHY; MEDICAL IMAGING; NATURAL LANGUAGE PROCESSING SYSTEMS; PIXELS; SUPPORT VECTOR MACHINES;

CLASSIFICATION (OF INFORMATION);

EID: 77955208756     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2010.5490373     Document Type: Conference Paper

References (8)

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