WiSARD is a weightless neural network model using RAMs to store the function computed by each neuron rather than storing it in connection weights between neurons. Non-linearity in WiSARD is implemented by a mapping that splits the binary input into tuples of bits and associate these tuples to neurons. In this work we apply the evolutionary µ + ? algorithm [1] to make evolve an initial population of mappings toward the generation of new mappings granting significant improvements in classification accuracy in the conducted experiments.
An evolutionary approach for optimizing weightless neural networks
Giordano Maurizio;De Gregorio Massimo
2019
Abstract
WiSARD is a weightless neural network model using RAMs to store the function computed by each neuron rather than storing it in connection weights between neurons. Non-linearity in WiSARD is implemented by a mapping that splits the binary input into tuples of bits and associate these tuples to neurons. In this work we apply the evolutionary µ + ? algorithm [1] to make evolve an initial population of mappings toward the generation of new mappings granting significant improvements in classification accuracy in the conducted experiments.File in questo prodotto:
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