Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control. (arXiv:2205.11956v1 [stat.ML])

Most machine learning methods depend on the tuning of hyper-parameters. For
kernel ridge regression (KRR) with the Gaussian kernel, the hyper-parameter is
the bandwidth. The bandwidth specifies the length-scale of the kernel and has
to be carefully selected in order to obtain a model with good generalization.
The default method for bandwidth selection is cross-validation, which often
yields good results, albeit at high computational costs. Furthermore, the
estimates provided by cross-validation tend to have very high variance,
especially when training …

arxiv kernel ml regression ridge