Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4641295 | Journal of Computational and Applied Mathematics | 2010 | 14 Pages |
Abstract
The problem of learning from data involving function values and gradients is considered in a framework of least-square regularized regression in reproducing kernel Hilbert spaces. The algorithm is implemented by a linear system with the coefficient matrix involving both block matrices for generating Graph Laplacians and Hessians. The additional data for function gradients improve learning performance of the algorithm. Error analysis is done by means of sampling operators for sample error and integral operators in Sobolev spaces for approximation error.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Lei Shi, Xin Guo, Ding-Xuan Zhou,