doctoralThesis
Aprendizagem semissupervisionada por meio de técnicas de Deep Learning e de Teoria da Informação
Fecha
2021-06-09Registro en:
LIMA, Bruno Vicente Alves de. Aprendizagem semissupervisionada por meio de técnicas de Deep Learning e de Teoria da Informação. 2021. 155f. Tese (Doutorado em Engenharia Elétrica e de Computação) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2021.
Autor
Lima, Bruno Vicente Alves de
Resumen
The expressive growth of modern data sets, combined with the difficulty of obtaining
information about labels, has made semi-supervised learning one of the problems of practical importance in modern data analysis. In most cases, obtaining a dataset with enough
examples to induce a classifier can be costly, as it is necessary to perform labeling of
the data by an expert. Unlabeled data is easier to obtain but more difficult to analyze
and interpret. In the semi-supervised learning problem, there is a database formed by a
small labeled part and a larger unlabelled part, with two possible aspects: semi-supervised
classification and semi-supervised clustering. With this, this work aims to apply models
that use deep learning techniques in semi-supervised learning. Using a deep autoencoder,
the data was transformed to feature space Z, and, from that, these data were clustered
and labeled, with the help of the labeled data. Information Theory Learning techniques
were applied to increase the robustness of the model proposed in this work. Experiments
performed showed the proposed model efficiency in labeling and classifying data after
training. It was also compared to other classic semi-supervised learning models, such
as co-training, tri-training, STRED and SEEDED K-means, as well as other more recent
works, showing the proposed model feasibility for the semi-supervised learning problem.
Finally, the model was applied to a real problem in remote sensing problem and stream
data classification.