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DVD (1) [Avatar] Offline
#1
Hi,

I've just read about the digit recognizer classifier and I have a question about the choice of the distance used.

I've heard about "Curse of dimensionality" and that Euclidean distance was not a good choice with too many features.
So I would like to know what's the biggest number a features so that Euclidean (or Manhattan) distance is OK.

And I am also interested in the number of examples I have to gather (to be able to learn something) if I have so many features.

I think it would be great to include a little something about this in the book (?)

Thanks,
David.