ISBN: 9783330022492 - development of new models for network traffic forecasting (7 resultados)

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  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2016

    3330022493 / 9783330022492

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    Librería: moluna, Greven, Alemaniamoluna

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    Cantidad disponible: Más de 20 disponibles

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    Editorial: LAP LAMBERT Academic Publishing, 2016

    3330022493 / 9783330022492

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    Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books

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    Paperback. Condición: Brand New. 112 pages. 8.66x5.91x0.26 inches. In Stock.

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2017

    3330022493 / 9783330022492

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    Librería: preigu, Osnabrück, Alemaniapreigu

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    Taschenbuch. Condición: Neu. Development of New Models for Network Traffic Forecasting | Derman Akgöl (u. a.) | Taschenbuch | 112 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330022492 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2016

    3330022493 / 9783330022492

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    Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books

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    paperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

  • Idioma: Inglés

    Editorial: LAP Lambert Academic Publishing Jan 2017, 2017

    3330022493 / 9783330022492

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    Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The purpose of this work is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has been utilized as training sets and the rest has been used as test sets. The performance of the forecasting models for the data sets has been assessed using Mean Absulote Percentage Error (MAPE). The results show that SVM and M5P based models usually perform better than the ones obtained by the other methods. 112 pp. Englisch.…

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing, 2016

    3330022493 / 9783330022492

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The purpose of this work is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has been utilized as training sets and the rest has been used as test sets. The performance of the forecasting models for the data sets has been assessed using Mean Absulote Percentage Error (MAPE). The results show that SVM and M5P based models usually perform better than the ones obtained by the other methods.…

  • Idioma: Inglés

    Editorial: LAP LAMBERT Academic Publishing Jan 2017, 2017

    3330022493 / 9783330022492

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    Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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    Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The purpose of this work is to forecast the amount of network traffic in Transmission Control Protocol/Internet Protocol (TCP/IP) -based networks by using different time lags and various machine learning methods including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Neural Network, M5P (a decision tree with linear regression functions at the nodes), Random Forest (RF), Random Tree (RT), and Reduced Error Prunning Error (REPTree), and statistical regression methods including Multiple Linear Regression (MLR) and Holt-Winters and compare the performance of statistical and machine learning methods. Two different Internet Service Providers' (ISPs) traffic data have been utilized to build traffic forecasting models. The first 66% of the data sets has been utilized as training sets and the rest has been used as test sets. The performance of the forecasting models for the data sets has been assessed using Mean Absulote Percentage Error (MAPE). The results show that SVM and M5P based models usually perform better than the ones obtained by the other methods.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 112 pp. Englisch.…