Iterative principles of recognition in probabilistic neural networks

Neural Networks

Volumn 21, Issue 6, 2008, Pages 838-846

  Grim, Jiří ^a,b   Hora, Jan ^a,b  

^a INSTITUTE OF INFORMATION THEORY AND AUTOMATION   (Czech Republic)

^b CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

Distribution mixtures; EM algorithm; Probabilistic neural networks; Recognition of numerals; Recurrent reasoning

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION MAKING; FEATURE EXTRACTION; MIXTURES; NEURONS; PATTERN RECOGNITION; PROBABILITY; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING; WATER PIPELINES;

BIOLOGICAL NEURONS; CONDITIONAL PROBABILITY DISTRIBUTIONS; DISTRIBUTION MIXTURES; DYNAMIC PROCESSES; DYNAMIC PROPERTIES; E-M ALGORITHMS; EM ALGORITHM; FINITE MIXTURES; INPUT PATTERNS; ITERATIVE SCHEMES; MIXTURE COMPONENTS; MONOTONICALLY; PROBABILISTIC APPROACHES; PROBABILISTIC DESCRIPTION; PROBABILISTIC NEURAL NETWORKS; RECOGNITION OF NUMERALS; RECURRENT REASONING; STATISTICAL PATTERN RECOGNITION;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; DECISION MAKING; DYNAMICS; NERVE CELL NETWORK; NEUROPHYSIOLOGY; PRIORITY JOURNAL; PROBABILITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; HUMANS; MODELS, STATISTICAL; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONS; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; RECOGNITION (PSYCHOLOGY);

EID: 50449091663     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2008.03.002     Document Type: Article

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