A large-deviation analysis for the maximum likelihood learning of tree structures

IEEE International Symposium on Information Theory - Proceedings

Volumn , Issue , 2009, Pages 1140-1144

  Tan, Vincent Y F ^a   Kumar, Animashree Anand ^a,b   Tongt, Lang ^b   Willsky, Alan S ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b School of Operations Research and Industrial Engineering   (United States)

Author keywords

Error exponents; Euclidean information theory; Large deviations; Tree structure learning

Indexed keywords

DEVIATION ANALYSIS; DISCRETE DISTRIBUTION; ERROR EXPONENT; ERROR EXPONENTS; ERROR PROBABILITIES; EUCLIDEAN; EUCLIDEAN INFORMATION THEORY; LARGE-DEVIATIONS; MAXIMUM LIKELIHOOD LEARNING; MUTUAL INFORMATIONS; NUMERICAL EXPERIMENTS; SUFFICIENT CONDITIONS; TREE STRUCTURE LEARNING; TREE STRUCTURES;

ACOUSTIC INTENSITY; EDUCATION; ERROR CORRECTION; FEATURE EXTRACTION; INFORMATION THEORY; PROBABILITY; SIGNAL TO NOISE RATIO;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 70449514560     PISSN: 21578102     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIT.2009.5206012     Document Type: Conference Paper

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