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EnglishIn 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: | ||
| 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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http://hdl.handle.net/11567/315616
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