@article{article_778804, title={Analyzing Classifier Performances Based on Implemented Expectation-Maximization Algorithm to Gaussian Mixture Model}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={26–37}, year={2020}, DOI={10.31590/ejosat.778804}, author={Cengiz, Korhan}, keywords={Bayes Sınflandırması, Yoğunluk Tahmini, EM Algoritması, GMM, k-NN, LDA}, abstract={Parametric density estimations i.e., maximum likelihood, mixture model, bayesian inference, maximum entropy are frequently used when type of distribution is known or predictable. Expectation-Maximization (EM) or a variable step learning algorithm are most successful ways for obtaining maximum likelihoods of distribution parameters. In this paper, we aim to present implementation of the EM algorithm to multidimensional Gaussian mixture model (GMM) that includes three different distributions. In this study, the statistical distribution is obtained from Gaussian distribution and parameters which are mean and covariance matrices for each distributions are used for estimation process. Original feature vectors and their estimates are compared in term of similarity as well as obtained results are presented and discussed in details. In addition, each distribution for bifurcated dataset is indicated. Finally, Bayesian, k-NN and Discriminant classifier methods are implemented to GMM and the performance of these methods are analyzed.}, publisher={Osman SAĞDIÇ}