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The paper deals with estimation of finite distribution mixtures which are practically important in cluster analysis, pattern recognition and other fields. After a brief survey of existing methods attention is confined to maximum-likelihood estimates, especially to an iterative procedure frequently discussed in the recent literature. It is shown that this… (More)

Abstrakt It is well known that log-likelihood function for finite mixtures usually has local maxima and therefore the iterative EM algorithm for maximum-likelihood estimation of mixtures may be starting-point dependent. In the present paper we propose a method of choosing initial parameters of mixtures which includes two stages: (a) computation of… (More)

The statistical pattern recognition based on Bayes formula implies the concept of mutually exclusive classes. This assumption is not applicable when we have to identify some non-exclusive properties and therefore it is unnatural in biological neural networks. Considering the framework of probabilistic neural networks we propose statistical identification of… (More)

We propose a new approach to diagnostic evaluation of screening mammograms based on local statistical texture models. The local evaluation tool has the form of a multivariate probability density of gray levels in a suitably chosen search window. First, the density function in the form of Gaussian mixture is estimated from data obtained by scanning of the… (More)

Assuming local and shift-invariant texture properties we describe the statistical dependencies between pixels by a joint probability density of gray-levels within a suitably chosen observation window. We estimate the unknown multi-variate density in the form of a Gaussian mixture of product components from data obtained by shifting the observation window.… (More)

A new method of texture modelling based on discrete distribution mixtures is proposed. Unlike some alternative approaches the statistical properties of textures are modelled by a discrete distribution mixture of product components. The univariate distributions in the products are represented in full generality by vectors of probabilities without any… (More)