Convergence rate of semi-supervised gradient learning algorithms
2015 ◽
Vol 13
(04)
◽
pp. 1550021
◽
Keyword(s):
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.
2019 ◽
Vol 9
(4)
◽
pp. p8817
Keyword(s):
2013 ◽
Vol 79
(4)
◽
pp. 347-357
◽
2006 ◽
Vol 28
(3)
◽
pp. 392-402
◽
2014 ◽
Vol 41
◽
pp. 53-64
◽
Keyword(s):
Keyword(s):
2021 ◽
Vol 15
(4)
◽
pp. 18-30