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Algoritmos de aprendizado semi-supervisionado baseados em grafos aplicados na bioinformáticaSemi-supervised machine learning algorithms based on graph applied in bioinformatics
(Universidade Estadual Paulista (Unesp), 2016)
Novelty detection in UAV images to identify emerging threats in eucalyptus crops
(2022-05-01)
Supervised learning-based methods can identify crop threats in the visual data collected by an Unmanned Aerial Vehicle (UAV). However, as these methods induce classification models from a finite set of a priori known ...
Interactive image segmentation of non-contiguous classes using particle competition and cooperation
(2015-01-01)
Semi-supervised learning methods employ both labeled and unlabeled data in their training process. Therefore, they are commonly applied to interactive image processing tasks, where a human specialist may label a few pixels ...
Rank-based self-training for graph convolutional networks
(2021-03-01)
Graph Convolutional Networks (GCNs) have been established as a fundamental approach for representation learning on graphs, based on convolution operations on non-Euclidean domain, defined by graph-structured data. GCNs and ...
Semi-supervised Segmentation Based on Error-Correcting Supervision
(2020-01-01)
Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation ...
Weakly supervised learning through rank-based contextual measures
(2020-01-01)
Machine learning approaches have achieved remarkable advances over the last decades, especially in supervised learning tasks such as classification. Meanwhile, multimedia data and applications experienced an explosive ...
Algoritmos de aprendizado semi-supervisionado baseados em grafos aplicados na bioinformática
(Universidade Estadual Paulista (Unesp), 2016-03-31)
As pesquisas realizadas para o Sequenciamento de Genomas, Proteômica, Sistemas Biológicos, Diagnósticos Médicos, entre outros, geram uma grande quantidade de dados, fazendo necessário o apoio de soluções computacionais ...
Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data
(Ieee, 2013-01-01)
Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must ...