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Old 04-11-2013, 01:32 AM
Moobb Moobb is offline
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Join Date: Apr 2013
Posts: 9
Default Re: Lecture 3 Q&A independence of parameter inputs

Many thanks for your answer and sorry for not including the reference for the lecture, it is 1:10:40 (can't include the tag directly right now). I believe I understood it now: if the input points are not independent than chances are it won't generalise well for the full set of possible inputs (taking an example from number identification, if you increase the size of number 8 by a factor of two, you won't learn anything new by doing so). Using the analogy to the coordinate systems, if the features are not independent than you may have less information than you suppose to have, but it may still be more practical than devising a feature that automatically incorporates only new elements, in practice the algorithm will benefit only from the new information incorporated from the feature. Guess there is a practical limit in terms of model complexity at some point? Or that you may end up incorporating just noise, so the use sometimes of dimensionality reduction prior to establishing your features? Thanks again!
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