English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Kernel ridge regression, KRR, is a generalization of linear ridge regressionthat is non-linear in the data, but linear in the parameters. The solution canbe obtained either as a closed-form solution, which includes a matrixinversion, or iteratively through gradient descent. Using the iterativeapproach opens up for changing the kernel during training, something that isinvestigated in this paper. We theoretically address the effects this has onmodel complexity and generalization. Based on our findings, we propose anupdate scheme for the bandwidth of translational-invariant kernels, where welet the bandwidth decrease to zero during training, thus circumventing the needfor hyper-parameter selection. We demonstrate on real and synthetic data howdecreasing the bandwidth during training outperforms using a constantbandwidth, selected by cross-validation and marginal likelihood maximization.We also show theoretically and empirically that using a decreasing bandwidth,we are able to achieve both zero training error in combination with goodgeneralization, and a double descent behavior, phenomena that do not occur forKRR with constant bandwidth but are known to appear for neural networks.

Solving Kernel Ridge Regression with Gradient Descent for a Non-Constant Kernel