Support Vector Machine for Internet Traffic Identification

In this paper a non linear system identification problem is addressed. A support vector regressor is used to solve the Internet traffic identification problem. We give a basic idea underlying support vector (SV) machine for regression, which is a novel type of learning machine based on statistical l...

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Bibliographic Details
Published in2007 14th IEEE International Conference on Electronics, Circuits and Systems pp. 351 - 354
Main Authors El Gonnouni, A., Lyhyaoui, A., Antari, J., El Jelali, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2007
Subjects
Internet
Kernel
Lagrangian functions
Linear systems
Machine learning
Neural networks
Support vector machine classification
Support vector machines
System identification
Systems engineering and theory
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Summary:In this paper a non linear system identification problem is addressed. A support vector regressor is used to solve the Internet traffic identification problem. We give a basic idea underlying support vector (SV) machine for regression, which is a novel type of learning machine based on statistical learning theory. Furthermore, we describe how SV regressor can be applied for non linear system identification. In our simulations results we present two type of kernel functions, the radial basis function (RBF), and the hyperbolic tangent, which are compared with the classical two-layer MLP (Multi-Layer- Perceptron) Neural Networks, trained to minimize a quadratic error objective with the back-propagation (BP) algorithm. The SV regressor outperforms the MLP and demonstrates its effectiveness for solving non linear system identification problems.
ISBN:142441377X
9781424413775
DOI:10.1109/ICECS.2007.4511002