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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 ...
Combining semi-supervised and active learning to recognize minority senses in a new corpus
(2015)
In this paper we study the impact of combining active learning with bootstrapping to grow a small annotated corpus from a different, unannotated corpus. The intuition underlying our approach is that bootstrapping includes ...
A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval
(2015-12-11)
The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on ...
Particle Competition and Cooperation in Networks for Semi-Supervised Learning
(Institute of Electrical and Electronics Engineers (IEEE), Computer Soc, 2013)
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 ...
Improving Semi-supervised Learning Through Optimum Connectivity
(Elsevier Sci LTDOxford, 2016)
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 ...