Comparative evaluation of probability density estimators for the probabilistic neural network

Hardware implementation of machine learning methods is often considered challenging, however, it brings major benefits, such as speed of operation and energy efficiency. Having in mind these benefits, we evaluate the performance and complexity of four probability density estimators (PDE) that we consider interesting for hardware implementation of the probabilistic neural network (PNN). We report results of a comparative evaluation of these PDEs for three different sizes of the PNN, evaluated in a common experimental protocol that makes use of the SPECT dataset. We conclude that two of the estimators are more appropriate for hardware implementation in FPGA.

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