Thread: Hw5 Q8 E_out
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Old 05-06-2013, 11:14 AM
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yaser yaser is offline
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Default Re: Hw5 Q8 E_out

Quote:
Originally Posted by arcticblue View Post
I am also a little unsure about exactly how this equation works:
E_{out} = \frac{1}{M} \sum_{i=1}^M \ln (1+e^{-Y_i w^\top X_i})

Obviously the more negative {-Y_i w^\top X_i} is the closer E_out is to zero which is good. So is w supposed to be normalized? I presume so because otherwise I could just scale w and then E_out becomes very small. And if it is normalized then the values I'm getting for E_in and E_out are both much greater than any of the options. (Maybe it's meant to be like that, if so it's quite unnerving.)
No normalization. The value of {\bf w} is determined iteratively by the specific algorithm given in the lecture. If {\bf w} 'agrees' with all the training examples, then indeed the algorithm will try to scale it up to get the value of the logistic function closer to a hard threshold. When you evaluate the quoted formula on a test set, {\bf w} is frozen and no scaling or any other change in it is allowed.
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