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Erschienen in:
2006 | OriginalPaper | Buchkapitel
A Competitive Approach to Neural Device Modeling: Support Vector Machines
verfasst von : Nurhan Türker, Filiz Güneş
Erschienen in: Artificial Neural Networks – ICANN 2006
Verlag: Springer Berlin Heidelberg
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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.