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Many weightless neural networks, such as WiSARD, are RAM-based classifiers that receive binary data as input. In order to convert raw data into binary input, several techniques are applicable. This work evaluates the impact of some of these binarization techniques on the accuracy of two types of classifiers: WiSARD model and WiSARD with bleaching mechanism. The binary encoding techniques explored...
The rapid change of trading values from tangible assets to Intelectual Property has put both businesses and academia in a race to acquire and protect the rights to exploit such property. This is mainly accomplished in the form of patent issuing by the governments, being time consuming and complicated due to the vast amount of documents that need to be analyzed in order to assert the novelty or validity...
This work proposes a new method, KernelCanvas, that is adequate to the Weightless Neural Model known asWiSARD for generating a fixed length binary input from spatio-temporal patterns. The method, based on kernel distances, issimple to implement and scales linearly to the number of kernels. Five different datasets were used to evaluate its performance in comparison with more widely employed approaches...
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