I'm aware of the concept of overfitting in Machine Learning. The main advice for dealing with it, usually is regularization.
Is there other practical advice to avoid overfitting?
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The main advice for dealing with it, usually is regularization. Is there other practical advice to avoid overfitting?
I thought what you are actually asking is what is the relation between regularization and overfitting.
The answer is that the strategies designed to reduce overfitting or test error are known collectively as regularization. So I thought the short answer to your question is an emphatic "no".
Parameter norm penalties
Norm penalities as constrained optimization
Parameter tying and parameter sharing
Bagging and other ensemble methods
Tangent distance, tagent prop, and manifold tagent classifier