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Old 01-13-2013, 09:14 AM
cygnids cygnids is offline
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Join Date: Jan 2013
Posts: 11
Default Re: Exercises and Problems

With respect to the discussion on Prob 1.10 thread here, it is noted that learning is not possible when the target function is a random function. On that note, what crosses my mind is how does one reconcile that statement with the thought that a purely random function could have more characterizable features after transformation to other domains? For eg., white noise maps to constant in spectral domain. Perhaps naively, if I had the a set of images with a reasonable proportion of fully noisy images, and choose to apply the ML apparatus in the frequency domain, I could learn a system to classify noisy vs not-noisy? Right?

It's very likely I'm picking on a point out of context here?
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