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Convergence rate of semi-supervised gradient learning algorithms.
- Source :
-
International Journal of Wavelets, Multiresolution & Information Processing . Jul2015, Vol. 13 Issue 4, p-1. 26p. - Publication Year :
- 2015
-
Abstract
- Semi-supervised learning deals with learning with a small amount labeled sample and a large amount of unlabeled sample to improve the learning ability. The purpose of the semi-supervised gradient learning is to increase the smoothness of the solution using unlabeled gradient data. In this paper, we study the semi-supervised kernel-based regularization scheme involving function gradient value. We show that the learning rate can be bounded by a K-functional with gradients of the function, which verify how the unlabeled gradient data quantitatively influences the learning rate. Some approaches from convex analysis play a key role in our error analysis. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02196913
- Volume :
- 13
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- International Journal of Wavelets, Multiresolution & Information Processing
- Publication Type :
- Academic Journal
- Accession number :
- 108564253
- Full Text :
- https://doi.org/10.1142/S0219691315500216