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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) ...
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) ...
Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift
(IEEE, 2012-01-01)
Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not ...
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 ...
Particle competition and cooperation for semi-supervised learning with label noise
(Elsevier B.V., 2015)
Model selection for semi-supervised clustering
(Athens, 2014-03)
Although there is a large and growing literature that tackles the semi-supervised clustering problem (i.e., using some labeled objects or cluster-guiding constraints like \must-link" or \cannot-link"), the evaluation of ...