Hardware implementation of Naïve Bayes classifier: A cost effective technique

Abstract

To distinguish the classes of unknown data, classification is an important technique. If implemented on hardware these techniques provide more accuracy, faster response in real time predictions than any software implementation. Existing hardware implementations are not at all cost effective, and require specific knowledge of hardware platform. In this paper a cost effective implementation of a statistical classifier has been proposed on Raspberry Pi, an open source hardware platform. This proposed implementation provides on board training of the classifier and real time class prediction. Moreover, it does not require knowledge about the hardware platform to classify the data.