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- W3048634555 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." @default.
- W3048634555 created "2020-08-18" @default.
- W3048634555 creator A5018586987 @default.
- W3048634555 date "2020-08-15" @default.
- W3048634555 modified "2023-09-25" @default.
- W3048634555 title "Sınıflandırıcı Performanslarının Gauss Karışım Modeline Uygulanan Beklenti-Maksimizasyon Algoritmasına Göre Analiz Edilmesi" @default.
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- W3048634555 doi "https://doi.org/10.31590/ejosat.778804" @default.
- W3048634555 hasPublicationYear "2020" @default.
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