Implementation of a SigmaBoost-based ensemble of SVM in a multiple processor system on chip

Abstract

This paper shows the effectiveness of a classifier ensemble composed of weak classifiers trained with a boosting algorithm implemented in a multiprocessor system on chip. The network is applied on the classification on thyroid disease diagnosis. The objective is to show that, even an FPGA with hardware restrictions, can be used to implement a complex problem, when parallel processing is used. To improve the system performance four soft processors were used with a shared memory.