Modelling of Web Domain Visits by Radial Basis Function Neural Networks and Support Vector Machine Regression

Abstract : The paper presents basic notions of web mining, radial basis function (RBF) neural networks and ε-insensitive support vector machine regression (ε- SVR) for the prediction of a time series for the website of the University of Pardubice. The model includes pre-processing time series, design RBF neural networks and ε-SVR structures, comparison of the results and time series prediction. The predictions concerning short, intermediate and long time series for various ratios of training and testing data. Prediction of web data can be benefit for a web server traffic as a complicated complex system.
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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-364 (Part II), pp.229-239, 2011, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-23960-1_28〉
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Vladimír Olej, Jana Filipová. Modelling of Web Domain Visits by Radial Basis Function Neural Networks and Support Vector Machine Regression. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-364 (Part II), pp.229-239, 2011, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-23960-1_28〉. 〈hal-01571473〉

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