Regularization networks with indefinite kernels
The author considers the least squares regression problem by coefficient-based regularization schemes with \(\ell_2\) norm square as the penalty generated by indefinite kernels. The key interesting idea is to introduce a pair of reproducing kernel Hilbert spaces for a singular value decomposition of the kernel integral. By methods from functional analysis, the author derives capacity independent error bounds and learning rates.
scientific article; zbMATH DE number 6137304