Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596809 | Neural Networks |
Keywords | Field | DocType |
graph theory,learning (artificial intelligence),neural nets,coupled integrate-and-fire neurons,feature space distribution,geometrical shape,label propagation,machine learning research area,neuronal synchrony,semisupervised learning | Graph theory,Graph,Data modeling,Synchronization,Feature vector,Semi-supervised learning,Pattern recognition,Computer science,Label propagation,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 7 |
PageRank | References | Authors |
0.57 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcos Quiles | 1 | 16 | 3.01 |
| Liang Zhao | 2 | 123 | 14.85 |
| Fabricio A. Breve | 3 | 8 | 1.00 |
| Anderson Rocha | 4 | 38 | 1.98 |