@conference {11905,
	title = {Non-parametric expectation maximization: a learning automata approach},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2003},
	volume = {3},
	year = {2003},
	month = {2003/10//},
	pages = {2996- 3001 vol.3 - 2996- 3001 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The famous expectation maximization technique suffers two major drawbacks. First, the number of components has to be specified apriori. Also, the expectation maximization is sensitive to initialization. In this paper, we present a new stochastic technique for estimating the mixture parameters. Parzen Window is used to estimate a discrete estimate of the PDF of the given data. Stochastic learning automata is then used to select the mixture parameters that minimize the distance between the discrete estimate of the PDF and the estimate of the expectation maximization. The validity of the proposed approach is verified using bivariate simulation data.},
	keywords = {Automatic speech recognition, bivariate simulation data, Computational modeling, Computer vision, Density functional theory, expectation maximization technique, learning automata, mixture parameters estimation, nonparametric expectation maximization, nonparametric statistics, optimisation, parameter estimation, Parzen Window, PDF, probability, probability density function, Speech processing, Speech recognition, stochastic learning automata, Stochastic processes, stochastic technique},
	isbn = {0-7803-7952-7},
	doi = {10.1109/ICSMC.2003.1244347},
	author = {Abd-Almageed, Wael and El-Osery,A. and Smith,C.E.}
}
