Label Propagation Through Neuronal Synchrony

Label Propagation Through Neuronal Synchrony

Author Quiles, Marcos Gonçalves Autor UNIFESP Google Scholar
Zhao, Liang Google Scholar
Breve, Fabricio A. Google Scholar
Rocha, Anderson Google Scholar
Institution Universidade Federal de São Paulo (UNIFESP)
Abstract Semi-Supervised Learning (SSL) is a machine learning research area aiming the development of techniques which are able to take advantage from both labeled and unlabeled samples. Additionally, most of the times where SSL techniques can be deployed, only a small portion of samples in the data set is labeled. To deal with such situations in a straightforward fashion, in this paper we introduce a semi-supervised learning approach based on neuronal synchrony in a network of coupled integrate-and-fire neurons. For that, we represent the input data set as a graph and model each of its nodes by an integrate-and-fire neuron. Thereafter, we propagate the class labels from the seed samples to unlabeled samples through the graph by means of the emerging synchronization dynamics. Experimentations on synthetic and real data show that the introduced technique achieves good classification results regardless the feature space distribution or geometrical shape.
Language English
Date 2010-01-01
Published in 2010 International Joint Conference On Neural Networks Ijcnn 2010. New York: Ieee, 8 p., 2010.
ISSN 1098-7576 (Sherpa/Romeo, impact factor)
Publisher Ieee
Extent 8
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000287421402076

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