Binary classification internet traffic par kaur jaspreet (6 résultats)
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Taschenbuch. Etat : Neu. Binary classification of Internet Traffic for Educational Institutions | Jaspreet Kaur (u. a.) | Taschenbuch | Paperback | 96 S. | Englisch | 2012 | LAP Lambert Academic Publishing | EAN 9783659301490 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]…bod[dot]de | Anbieter: preigu.
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paperback. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The infinite number of websites in the internet world can be classified into a number of categories depending on the purpose for classification.In educational institutions, it is required that the internet facility be used for academic…purposes only. This can happen through accurate classification of network traffic into two classes, educational websites and non-educational websites. In educational institutes, for the optimum use of network resources the use of non-educational websites should be banned. In our research work, we explore the use of ML (machine learning) algorithms for classification of internet traffic into two classes. We discuss the basis for differentiating the traffic into these two classes, and investigate the impact of flexibility in making such differentiation on the performance of selected five ML algorithms. We also investigate the impact of class error in data set and propose a generalized feature subset which is robust against all these impacts. Results show that Bayes Net algorithm performs consistently for intended classification of internet traffic in terms of classification accuracy, training time of classifiers, recall and precision. 96 pp. Englisch.
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kaur JaspreetI, Jaspreet Kaur am currently working as an Assistant Professor at CGC Landran, Punjab, India. I did my ME from UIET, PU Chandigarh, India. This work would not have been possible without the ki…nd support of my Guide Mr. .
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The infinite number of websites in the internet world can be classified into a number of categories depending on the purpose for classification.In educational institutions, it is required that the internet facility be used for academic purp…oses only. This can happen through accurate classification of network traffic into two classes, educational websites and non-educational websites. In educational institutes, for the optimum use of network resources the use of non-educational websites should be banned. In our research work, we explore the use of ML (machine learning) algorithms for classification of internet traffic into two classes. We discuss the basis for differentiating the traffic into these two classes, and investigate the impact of flexibility in making such differentiation on the performance of selected five ML algorithms. We also investigate the impact of class error in data set and propose a generalized feature subset which is robust against all these impacts. Results show that Bayes Net algorithm performs consistently for intended classification of internet traffic in terms of classification accuracy, training time of classifiers, recall and precision.Books on Demand GmbH, Überseering 33, 22297 Hamburg 96 pp. Englisch.
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The infinite number of websites in the internet world can be classified into a number of categories depending on the purpose for classification.In educational institutions, it is required that the internet facility be used for academic purpo…ses only. This can happen through accurate classification of network traffic into two classes, educational websites and non-educational websites. In educational institutes, for the optimum use of network resources the use of non-educational websites should be banned. In our research work, we explore the use of ML (machine learning) algorithms for classification of internet traffic into two classes. We discuss the basis for differentiating the traffic into these two classes, and investigate the impact of flexibility in making such differentiation on the performance of selected five ML algorithms. We also investigate the impact of class error in data set and propose a generalized feature subset which is robust against all these impacts. Results show that Bayes Net algorithm performs consistently for intended classification of internet traffic in terms of classification accuracy, training time of classifiers, recall and precision.
