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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.
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Gaussian Mixture Model : Application to Medical Image Classification
Verlag:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139987954
ISBN 13: 9786139987955
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Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 56 | Sprache: Englisch | Produktart: Bücher. Bestandsnummer des Verkäufers 33514047/1
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Gaussian Mixture Model
Verlag:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139987954
ISBN 13: 9786139987955
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar A. VigneshA. Vignesh Kumar, Completed M.E(CSE) & doing Ph.D from Anna University,Chennai and having 5 Years of Academic Experience.Gaussian Mixture Model (GMM) is the probabilistic model, it works well with the classificati. Bestandsnummer des Verkäufers 266932471
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Gaussian Mixture Model
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LAP LAMBERT Academic Publishing Dez 2018, 2018
ISBN 10: 6139987954
ISBN 13: 9786139987955
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Taschenbuch. Zustand: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch. Bestandsnummer des Verkäufers 9786139987955
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Gaussian Mixture Model
Verlag:
LAP LAMBERT Academic Publishing Dez 2018, 2018
ISBN 10: 6139987954
ISBN 13: 9786139987955
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 56 pp. Englisch. Bestandsnummer des Verkäufers 9786139987955
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Gaussian Mixture Model : Application to Medical Image Classification
Verlag:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139987954
ISBN 13: 9786139987955
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786139987955
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Gaussian Mixture Model: Application to Medical Image Classification
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LAP Lambert Academic Publishing, 2019
ISBN 10: 6139987954
ISBN 13: 9786139987955
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Paperback. Zustand: Brand New. 8.70x6.02x0.28 inches. In Stock. Bestandsnummer des Verkäufers zk6139987954
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