Banca de DEFESA: BRUNO VICENTE ALVES DE LIMA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : BRUNO VICENTE ALVES DE LIMA
DATE: 09/06/2021
TIME: 09:00
LOCAL: Virtual Pelo Google Meet
TITLE:

Semi-supervised Learning by Deep Learning Techniques and Information Theory

KEY WORDS:

Semi-supervised, Deep Learning, Labeling, Clustering, Classification, Information Theory.

PAGES: 70
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUMMARY:

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 set of data with enough examples to induce a classifier, can be costly, as it is necessary to carry out data labeling by a specialist. Unlabeled data is easier to obtain but more difficult to analyze and interpret. In of semi-supervised learning problem, there is a database formed by a small part labeled and a larger part not labeled, being possible two 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, where a neural network is trained, in this case, an autoencoder using unlabeled data. Then, an additional layer is embedded in the encoder. This new layer has its weights initialized by the K-means ++ algorithm and adjusted through the backpropagation algorithm using information theory learning. The labeled data is assigned to the clusters generated by the encoder, influencing the unlabeled, cluster by cluster, thus labeling the non-labeled data that was previously clustered. With the experiments carried out, it was noted that the satisfactory performanceof the proposed model when compared with other semi-supervised algorithms, both the classics such as self-training and co-training, as well as other more recent works, showing the proposed model feasibility for the learning semi-supervised problem.

BANKING MEMBERS:
Presidente - 347628 - ADRIAO DUARTE DORIA NETO
Externo ao Programa - 1669545 - DANIEL SABINO AMORIM DE ARAUJO
Externo à Instituição - IVAN NUNES DA SILVA - USP
Externo à Instituição - JORGE DANTAS DE MELO - UFRN
Externo à Instituição - VINICIUS PONTE MACHADO - UFPI