ISBN: 9783844399240 - hybrid methods in feature selection: a data classification perspective: hybrid feature selection methods are the proven methods for large scale feature selection (9 risultati)

Condizione: New. pp. 64.

Taschenbuch. Condizione: Neu. Hybrid Methods in Feature Selection: A Data Classification Perspective | Hybrid Feature Selection Methods are the proven methods for Large Scale Feature Selection | Senthamarai Kannan Subramanian | Taschenbuch | 64 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844399240 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In recent years, data have become increasingly larger in both number of instances and number of features in many applications. This enormity may cause serious problems to many machine learning algorithms with respect to scalability and learning performance. Therefore, feature selection is essential for the machine learning algorithms while handling high dimensional datasets. Many traditional search methods have shown promising results in a number of feature selection problems. However, as the number of features increases extremely, most of these existing methods face the problem of intractable computational time. Since no single feature selection method could handle all requirements of feature selection in real world datasets, hybrid methods prsented here are the tested methods for effecive Feature Selection.One viable option is to apply a ranking feature selection method to obtain a manageable number of top ranked features which could be further handled by traditional feature selection methods for further analysis. 64 pp. Englisch.…

Condizione: New. Print on Demand pp. 64 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In recent years, data have become increasingly larger in both number of instances and number of features in many applications. This enormity may cause serious problems to many machine learning algorithms with respect to scalability and learning performance. Therefore, feature selection is essential for the machine learning algorithms while handling high dimensional datasets. Many traditional search methods have shown promising results in a number of feature selection problems. However, as the number of features increases extremely, most of these existing methods face the problem of intractable computational time. Since no single feature selection method could handle all requirements of feature selection in real world datasets, hybrid methods prsented here are the tested methods for effecive Feature Selection.One viable option is to apply a ranking feature selection method to obtain a manageable number of top ranked features which could be further handled by traditional feature selection methods for further analysis.…

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Subramanian Senthamarai KannanDr S.SENTHAMARAI KANNAN is presently working as Assistant Professor with Thiagarajar College of Engineering , India. He has published nine papers in reputed international journals including Elsevier K. …

Condizione: New. PRINT ON DEMAND pp. 64.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In recent years, data have become increasingly larger in both number of instances and number of features in many applications. This enormity may cause serious problems to many machine learning algorithms with respect to scalability and learning performance. Therefore, feature selection is essential for the machine learning algorithms while handling high dimensional datasets. Many traditional search methods have shown promising results in a number of feature selection problems. However, as the number of features increases extremely, most of these existing methods face the problem of intractable computational time. Since no single feature selection method could handle all requirements of feature selection in real world datasets, hybrid methods prsented here are the tested methods for effecive Feature Selection.One viable option is to apply a ranking feature selection method to obtain a manageable number of top ranked features which could be further handled by traditional feature selection methods for further analysis.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.…