The WiSARD classifier

WiSARD is a weightless neural model which essentially uses look up tables to store the function computed by each neuron rather than storing it in weights of neuron connections. Although WiSARD was originally conceived as a pattern recognition device mainly focusing on image processing, in this work we show how it is possible to build a multi-class classifier method in Machine Learning (ML) domain based on WiSARD that shows equivalent performances to ML state-of-the-art methods.