1
$\begingroup$

I saw here: http://www.cs.cmu.edu/~ninamf/ML11/lect0906.pdf

Intuitively, if “n” is large but most features are irrelevant (i.e. target is sparse but examples are dense), then Winnow is better because adding irrelevant features increases L2(X) but not L∞(X). On the other hand, if the target is dense and examples are sparse, then Perceptron is better.

Why adding irrelevant features increases L2(X) but not L∞(X)?

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.