Abordagem Semissupervisionada usando Deep Learning Aplicada á Rotulação e Classificação de Dados

Bruno Vicente Alves de Lima, Lucia Emilia Soares Silva, Adrião Duarte Doria Neto, Vinicius Ponte Machado

Resumo


Large-scale data generation has brought the need for the development
of intelligent techniques capable of analyzing this data automatically. In this
sense, this paper proposes a semisupervisioned classification model capable of
labeling unlabeled data from a few labeled examples. For this, a deep neural
network was trained with labeled and unlabeled examples, simutaneally. The
experiments performed show that the model is efficient in labeling data and
predicting new examples.


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