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Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2020)
This work presents an investigation on how to define Neural Networks (NN) architectures adopting a data-driven approach using clustering to create sub-labels to facilitate the learning process and to discover the number of neurons needed to compose the layers. We also increase the depth of the model aiming to represent the samples better, the more in-depth it flows into the model. We hypothesize that the clustering process identifies sub-regions in the feature space in which the samplesdoi:10.5753/eniac.2020.12160 fatcat:jsbyremcg5dv3dtkosxfkulyxi