No Access
Convergence rate of semi-supervised gradient learning algorithms
https://doi.org/10.1142/S0219691315500216Cited by:6 (Source: Crossref)
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.
AMSC: 41A25, 90C25, 68Q32, 68T05, 46E22
