# #StackBounty: #cross-validation #ridge-regression #vc-dimension Rademacher Bound, An Alternative to Cross Validation for Ridge?

### Bounty: 100

Below is a theorem from the book “Foundations of Machine Learning”.

It specifies the generalization bounds for Kernel Ridge Regression by making use of the Rademacher Complexity on linear models. $$R(h)$$ is the generalization error, and $$hat{R}(h)$$ is the empirical error. Now pretty much everything is either known to us, picked by us, or can be calculated by us. $$m$$ is the number of training samples.

Instead of finding the right penalty $$Lambda$$ via cross validation, can we simply pick the $$Lambda$$ that minimizes the right hand side of the inequality? What should be the $$delta$$ value to be set in order to achieve best predictive result? How to choose $$r$$ as tight as possible?

Is this an alternative to Cross Validation for Kernel Ridge (or just Ridge) Regression?

Get this bounty!!!

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