Recursive unsupervised learning of finite mixture models

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 26, Issue 5, 2004, Pages 651-656

  Zivkovic, Zoran ^a   Van der Heijden, Ferdinand ^b  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

EM algorithm; Finite mixtures; Model selection; Online (recursive) estimation; Unsupervised learning

Indexed keywords

APPROXIMATION THEORY; LEARNING ALGORITHMS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING; RANDOM PROCESSES;

EXPECTATION MAXIMIZATION ALGORITHM; FINITE MIXTURE MODELS; MAXIMUM A POSTERIORI SOLUTION; RECURSIVE UNSUPERVISED LEARNING;

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COMPARATIVE STUDY; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER SIMULATION; EVALUATION; INFORMATION RETRIEVAL; MATHEMATICAL COMPUTING; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER GRAPHICS; COMPUTER SIMULATION; INFORMATION STORAGE AND RETRIEVAL; LIKELIHOOD FUNCTIONS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STOCHASTIC PROCESSES; USER-COMPUTER INTERFACE;

EID: 3042585857     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2004.1273970     Document Type: Article

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