A source coding approach to classification by vector quantization and the principle of minimum description length

Data Compression Conference Proceedings

Volumn 2002-January, Issue , 2002, Pages 382-391

  Li, J ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Clustering algorithms; Data compression; Probability; Prototypes; Random variables; Source coding; Statistics; Testing; Training data; Vector quantization

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; CODES (SYMBOLS); DATA COMPRESSION; ENCODING (SYMBOLS); OPTIMIZATION; PROBABILITY; RANDOM VARIABLES; STATISTICS; TESTING; VECTORS;

CONDITIONAL DISTRIBUTION; LEARNING VECTOR QUANTIZATION; MINIMUM DESCRIPTION LENGTH; MISCLASSIFICATION RATES; PROTOTYPES; SOURCE-CODING; SUPERVISED CLASSIFICATION; TRAINING DATA;

VECTOR QUANTIZATION;

EID: 84863345028     PISSN: 10680314     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DCC.2002.999978     Document Type: Conference Paper

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