We investigate machine learning for the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernels. We provide some estimates for the learning raters of both regression and classification when the hypothesis spaces are sample dependent. Under a weak condition on the kernels we derive learning error by estimating the rate of some K-functional when the target functions belong to the range of some Hilbert-Schmidt integral operator.
Digital Object Identifier (DOI)
Baohuai, Sheng; Peixin, Ye; and Wangke, Yu
"Convergence Rate of Coefficient Regularized Kernel-based Learning Algorithms,"
Applied Mathematics & Information Sciences: Vol. 08:
2, Article 51.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol08/iss2/51