A competitive approach to neural device modeling: support vector machines

Authors:
Nurhan Türker;Filiz Güneş
Affiliations:
Electrical and Electronics Faculty, Department of Electronics and Communication Engineering, Yıldız Technical University, Yıldız, Istanbul, Turkey;Electrical and Electronics Faculty, Department of Electronics and Communication Engineering, Yıldız Technical University, Yıldız, Istanbul, Turkey
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

Support Vector Machines (SVM) are a system for efficiently training linear learning machines in the kernel induced feature spaces, while respecting the insights provided by the generalization theory and exploiting the optimization theory. In this work, Support Vector Machines are employed for the nonlinear regression. The nonlinear regression ability of the Support Vector Machines has been demonstrated by forming the SVM model of a microwave transistor and it has been compared with its neural model.