Using Variable Neighborhood Search to Improve the Support Vector Machine Performance in Embedded Automotive Applications

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.

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Titolo: Using Variable Neighborhood Search to Improve the Support Vector Machine Performance in Embedded Automotive Applications
Autori: 
ANGUITA, DAVIDE
GHIO, ALESSANDRO
RIDELLA, SANDRO
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Data di pubblicazione: 2008
Abstract: 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.
Handle: http://hdl.handle.net/11567/315616
ISBN: 9781424418206
Appare nelle tipologie:04.01 - Contributo in atti di convegno

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