Shared kernel models for class conditional density estimation

IEEE Transactions on Neural Networks

Volumn 12, Issue 5, 2001, Pages 987-997

  Titsias, Michalis K ^b   Likas, Aristidis C ^a,b  

^a IEEE   (Greece)

^b UNIVERSITY OF IOANNINA   (Greece)

Author keywords

Classification; Density estimation; Expectation maximization (EM) algorithm; Mixture models; Probabilistic neural networks; Radial basis function (RBF) network

Indexed keywords

CONDITIONAL DENSITY ESTIMATION; EXPECTATION MINIMIZATION ALGORITHMS; MIXTURE DENSITY MODELS; PROBABILISTIC NEURAL NETWORKS; SHARED KERNEL MODELS;

CONSTRAINT THEORY; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; PROBABILITY DENSITY FUNCTION;

RADIAL BASIS FUNCTION NETWORKS;

EID: 0035440306     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.950129     Document Type: Article

References (15)

1. Maximum likelihood estimation from incomplete data via the EM algorithm
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7. A Kurtosis-based dynamic approach to Gaussian mixture modeling