Informatica

Influence of projection pursuit on classification errors and estimates of a posteriori probabilities from the sample is considered. Observed random variable is supposed to satisfy a multidimensional Gaussian mixture model. Presented computer simulation results show that for comparatively small sample size classification using projection pursuit algorithm gives better accuracy of estimates of a posteriori probabilities and less classification error.

Efficiency of one automatic estimation and c1usterization procedure of one-dimensional Gaussian mixture which combines EM algorithm with non-parametric estimation is considered. The paper is based on mathematical methods of statistical estimation of a mixture of Gaussian distributions presented by R. Rudzkis and M. Radavičius (1995). The main result of the implementation of the mathematical methods is completely automatic procedure which can start from no information about unknown parameters and finish with final mixture model (tested for adequacy).