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Semi-supervised learning guided by the modularity measure in complex networks
(ELSEVIER SCIENCE BVAMSTERDAM, 2012)
Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is ...
Hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification
(Springer, 2019-03-01)
Usually, image classification methods have supervised or unsupervised learning paradigms. While unsupervised methods do not need training data, the meanings behind the classified elements are not explicitly know. Conversely, ...
Particle competition and cooperation for semi-supervised learning with label noise
(Elsevier B.V., 2015-07-21)
Semi-supervised learning methods are usually employed in the classification of data sets where only a small subset of the data items is labeled. In these scenarios, label noise is a crucial issue, since the noise may easily ...
Multi-label semi-supervised classification through optimum-path forest
(Elsevier B.V., 2018-10-01)
Multi-label classification consists of assigning one or multiple classes to each sample in a given dataset. However, the project of a multi-label classifier is usually limited to a small number of supervised samples as ...
Particle Competition and Cooperation in Networks for Semi-Supervised Learning
(IEEE COMPUTER SOCLOS ALAMITOS, 2012)
Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) ...
Particle Competition and Cooperation in Networks for Semi-Supervised Learning
(Institute of Electrical and Electronics Engineers (IEEE), Computer Soc, 2012-09-01)
Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) ...
Semi-supervised learning with connectivity-driven convolutional neural networks
(2019-12-01)
The annotation of large datasets is an issue whose challenge increases as the number of labeled samples available to train the classifier reduces in comparison to the amount of unlabeled data. In this context, semi-supervised ...
A semi-supervised classification technique based on interacting forces
(ElsevierAmsterdam, 2014-03-15)
Semi-supervised learning is a classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by ...
Semi-supervised learning with convolutional neural networks for UAV images automatic recognition
(Elsevier B.V., 2019-09-01)
The annotation of large datasets is an issue whose challenge increases as the number of labeled samples available to train the classifier reduces in comparison to the amount of unlabeled data. In this context, semi-supervised ...
Improving semi-supervised learning through optimum connectivity
(Elsevier B.V., 2016-12-01)
The annotation of large data sets by a classifier is a problem whose challenge increases as the number of labeled samples used to train the classifier reduces in comparison to the number of unlabeled samples. In this ...