Name
Title | ||
|---|---|---|
Using Variable Neighborhood Search To Improve The Support Vector Machine Performance In Embedded Automotive Applications |
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IJCNN.2008.4633918 | 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 |
Keywords | Field | DocType |
automotive engineering,artificial neural networks,kernel,nickel,digital signal processors,digital signal processor,field programmable gate arrays,support vector machines,field programmable gate array,machine vision,digital signal processing,classification algorithms,support vector machine,testing,hardware,feed forward,feedforward | Kernel (linear algebra),Variable neighborhood search,Computer science,Digital signal processor,Support vector machine,Field-programmable gate array,Artificial intelligence,Artificial neural network,Statistical classification,Machine learning,Metaheuristic | Conference |
ISSN | Citations | PageRank |
2161-4393 | 5 | 0.44 |
References | Authors | |
22 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Alba | 1 | 3796 | 242.34 |
| Davide Anguita | 2 | 1001 | 70.58 |
| Alessandro Ghio | 3 | 667 | 35.71 |
| Sandro Ridella | 4 | 677 | 140.62 |