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.

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Enrique Alba, Davide Anguita, Alessandro Ghio, Sandro Ridella
• Keywords: Artificial Intelligence, Considerable Performance Improvements, IJCNN 2008, Programmable Gate Arrays, Variable Neighborhood Search