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.