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Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier: Using Information Based Distance Metric - Brossura
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Machine learning is the study of algorithms that automatically improve their performance with experience. That can provide significant competitive advantages to many organizations by exploiting the potential of large data volume. Intelligently analyzed data is a valuable resource. At the heart of performance is classification accuracy in this specified task. Mostly a crucial problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. The classification of data is based on the set of data feature used. The feature selection can provide optimizing performance by using Genetic Algorithm and strongly effect in classification. In making to get improved classification accuracy, author has taken advantage with using hybrid of machine learning methods rather than use of only machine learning approach. Author proposed Information based distance metric to overwhelm one of the weak points of k nearest neighbor classifier. Moreover it provide the comparison of the result of Information based distance metric and Euclidean distance metric on both majority voting and similarity score summing.
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Informazioni sull'autore
The author was born in Taungoo Township in Myanmar. In 1995, she got B.Sc. (Honours) Chemistry from Yangon University . In 1999, she got M.I.Sc and in 2004, she got Ph.D (IT) from University of Computer Studies, Yangon. In 2010, she got Diploma in Software Application from SIBIT, India. Now she is working as an Associate Professor at the U.C.S.Y.
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Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier
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LAP LAMBERT Academic Publishing Nov 2015, 2015
ISBN 10: 365979029X
ISBN 13: 9783659790294
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine learning is the study of algorithms that automatically improve their performance with experience. That can provide significant competitive advantages to many organizations by exploiting the potential of large data volume. Intelligently analyzed data is a valuable resource. At the heart of performance is classification accuracy in this specified task. Mostly a crucial problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. The classification of data is based on the set of data feature used. The feature selection can provide optimizing performance by using Genetic Algorithm and strongly effect in classification. In making to get improved classification accuracy, author has taken advantage with using hybrid of machine learning methods rather than use of only machine learning approach. Author proposed Information based distance metric to overwhelm one of the weak points of k nearest neighbor classifier. Moreover it provide the comparison of the result of Information based distance metric and Euclidean distance metric on both majority voting and similarity score summing. 108 pp. Englisch. Codice articolo 9783659790294
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Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 365979029X
ISBN 13: 9783659790294
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Brossura
Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Phyu SabaiThe author was born in Taungoo Township in Myanmar. In 1995, she got B.Sc. (Honours) Chemistry from Yangon University . In 1999, she got M.I.Sc and in 2004, she got Ph.D (IT) from University of Computer Studies, Yangon. In . Codice articolo 158605504
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Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier
Editore:
LAP LAMBERT Academic Publishing Nov 2015, 2015
ISBN 10: 365979029X
ISBN 13: 9783659790294
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine learning is the study of algorithms that automatically improve their performance with experience. That can provide signi¿cant competitive advantages to many organizations by exploiting the potential of large data volume. Intelligently analyzed data is a valuable resource. At the heart of performance is classi¿cation accuracy in this speci¿ed task. Mostly a crucial problem in machine learning is identifying a representative set of features from which to construct a classi¿cation model for a particular task. The classi¿cation of data is based on the set of data feature used. The feature selection can provide optimizing performance by using Genetic Algorithm and strongly effect in classi¿cation. In making to get improved classi¿cation accuracy, author has taken advantage with using hybrid of machine learning methods rather than use of only machine learning approach. Author proposed Information based distance metric to overwhelm one of the weak points of k nearest neighbor classi¿er. Moreover it provide the comparison of the result of Information based distance metric and Euclidean distance metric on both majority voting and similarity score summing.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 108 pp. Englisch. Codice articolo 9783659790294
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Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier : Using Information Based Distance Metric
Editore:
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 365979029X
ISBN 13: 9783659790294
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine learning is the study of algorithms that automatically improve their performance with experience. That can provide significant competitive advantages to many organizations by exploiting the potential of large data volume. Intelligently analyzed data is a valuable resource. At the heart of performance is classification accuracy in this specified task. Mostly a crucial problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. The classification of data is based on the set of data feature used. The feature selection can provide optimizing performance by using Genetic Algorithm and strongly effect in classification. In making to get improved classification accuracy, author has taken advantage with using hybrid of machine learning methods rather than use of only machine learning approach. Author proposed Information based distance metric to overwhelm one of the weak points of k nearest neighbor classifier. Moreover it provide the comparison of the result of Information based distance metric and Euclidean distance metric on both majority voting and similarity score summing. Codice articolo 9783659790294
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Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier | Using Information Based Distance Metric
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 365979029X
ISBN 13: 9783659790294
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Taschenbuch. Condizione: Neu. Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier | Using Information Based Distance Metric | Sabai Phyu | Taschenbuch | 108 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659790294 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 104092392
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Hybridization of Genetic Algorithm and K Nearest Neighbor Classifier: Using Information Based Distance Metric
Editore:
LAP LAMBERT Academic Publishing, 2015
ISBN 10: 365979029X
ISBN 13: 9783659790294
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paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Codice articolo ERICA829365979029X6
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