9786202917315 - improved parametric and nonparametric classification techniques: the box-cox transformation and bootstrap approach de rahman, md. mahabubur; majumder, ajit kumar (10 resultados)
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Taschenbuch. Condición: Neu. Improved Parametric and Nonparametric Classification Techniques | The Box-Cox Transformation and Bootstrap Approach | Md. Mahabubur Rahman (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202917315 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengerich…er Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This text considers different parametric and nonparametric classification techniques to classify objects, and make a comparative study among these techniques. In most of the situations, classification techniques give few misclassifi…cations under large samples as well as under the normal populations. If the data set comes from the non-normal populations, then we apply Box-Cox transformation to transform this data set into near normal. Hence, we investigate the effect of Box-Cox transformation and see that Box-Cox transformed data generates better discrimination and classification techniques. Also if the sample size is small, then we use the Bootstrap approach for classifying objects, and investigate that the Bootstrap classification technique used in this analysis performs better than the usual techniques of small samples. There is no unique classification technique that is suitable for all the situations, also examines that nonparametric classification techniques perform better than the parametric classification techniques, whereas the Neural Network classification technique gives optimum solutions among the nonparametric classification techniques. 124 pp. Englisch.
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rahman Md. MahabuburRight now, Dr. Md. Mahabubur Rahman is working as an Associate Professor in the Department of Statistics at Islamic University, Bangladesh. He received a Ph.D. degree in Statistics f…rom KAU, KSA. He has published .
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This text considers different parametric and nonparametric classification techniques to classify objects, and make a comparative study among these techniques. In most of the situations, classification techniques give few misclassificati…ons under large samples as well as under the normal populations. If the data set comes from the non-normal populations, then we apply Box-Cox transformation to transform this data set into near normal. Hence, we investigate the effect of Box-Cox transformation and see that Box-Cox transformed data generates better discrimination and classification techniques. Also if the sample size is small, then we use the Bootstrap approach for classifying objects, and investigate that the Bootstrap classification technique used in this analysis performs better than the usual techniques of small samples. There is no unique classification technique that is suitable for all the situations, also examines that nonparametric classification techniques perform better than the parametric classification techniques, whereas the Neural Network classification technique gives optimum solutions among the nonparametric classification techniques.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This text considers different parametric and nonparametric classification techniques to classify objects, and make a comparative study among these techniques. In most of the situations, classification techniques give few misclassificatio…ns under large samples as well as under the normal populations. If the data set comes from the non-normal populations, then we apply Box-Cox transformation to transform this data set into near normal. Hence, we investigate the effect of Box-Cox transformation and see that Box-Cox transformed data generates better discrimination and classification techniques. Also if the sample size is small, then we use the Bootstrap approach for classifying objects, and investigate that the Bootstrap classification technique used in this analysis performs better than the usual techniques of small samples. There is no unique classification technique that is suitable for all the situations, also examines that nonparametric classification techniques perform better than the parametric classification techniques, whereas the Neural Network classification technique gives optimum solutions among the nonparametric classification techniques.
