Name
Abstract | ||
|---|---|---|
•A new application for the semi-supervised Optimum-Path Forest classifier.•New highlights in how to improve deep networks using semi-supervised learning.•Promising and accurate results.•More contributions to semi-supervised-related literature.•An extensive experimental evaluation is conducted. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patrec.2019.08.012 | Pattern Recognition Letters |
Keywords | Field | DocType |
Optimum-path forest,Semi-supervised learning,Convolutional neural networks | Training set,Semi-supervised learning,Annotation,Pattern recognition,Convolutional neural network,Convolution,Artificial intelligence,Labeled data,Artificial neural network,Classifier (linguistics),Mathematics | Journal |
Volume | ISSN | Citations |
128 | 0167-8655 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Willian Paraguassu Amorim | 1 | 24 | 4.52 |
| Gustavo H. Rosa | 2 | 47 | 8.00 |
| Rogério Thomazella | 3 | 0 | 0.34 |
| José Eduardo Cogo Castanho | 4 | 0 | 0.34 |
| Fábio Romano Lofrano Dotto | 5 | 0 | 0.34 |
| Oswaldo Pons Rodrigues Júnior | 6 | 0 | 0.34 |
| Aparecido Nilceu Marana | 7 | 83 | 13.80 |
| João P. Papa | 8 | 689 | 46.87 |