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Using Variable Neighborhood Search to improve the Support Vector Machine performance in embedded automotive applications

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4 Author(s)
Alba, E. ; Dept. de Lenguajes y Cienc. de la Comput., Univ. of Malaga, Malaga ; Anguita, D. ; Ghio, A. ; Ridella, S.

In this work we show that a metaheuristic, the variable neighborhood search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the support vector machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource-limited hardware devices, such as field programmable gate arrays (FPGAs) and digital signal processors (DSPs). The proposal has been tested on a machine-vision benchmark dataset for embedded automotive applications, showing considerable performance improvements respect to previously used techniques.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008