Edité par LAP Lambert Academic Publishing, 2019
ISBN 10 : 6139987954 ISBN 13 : 9786139987955
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Ajouter au panierPaperback. Etat : Brand New. 8.70x6.02x0.28 inches. In Stock.
Edité par LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6139987954 ISBN 13 : 9786139987955
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. Gaussian Mixture Model | Application to Medical Image Classification | A. Vignesh Kumar (u. a.) | Taschenbuch | 56 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139987955 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Edité par LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6139987954 ISBN 13 : 9786139987955
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Edité par LAP LAMBERT Academic Publishing, 2018
ISBN 10 : 6139987954 ISBN 13 : 9786139987955
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classification and parameter estimation strategy. In this Maximum Likelihood Estimation (MLE) based on Expectation Maximization (EM) is being used for the parameter estimation approach and the estimated parameters are being used for the training and the testing of the images for their normality and the abnormality. With the mean and the covariance calculated as the parameters they are used in the Gaussian Mixture Model (GMM) based training of the classifier. Support Vector Machine a discriminative classifier and the Gaussian Mixture Model a generative model classifier are the two most popular techniques used in this work. The performance of the classification strategy of both the classifiers used have a better proficiency when compared to the other classifiers. By combining the SVM and GMM we could be able to classify at a better level since estimating the parameters through the GMM has a very few amount of features and hence it is not needed to use any of the feature reduction techniques.