Quote:
Originally Posted by Sweater Monkey
I feel like I'm overthinking Exercise 4.7 (b) and I am hoping for a little bit of insight.
My gut instinct says that ![Var[E_{\text{val}}(g^-)] = \frac{1}{K} (P[g^-(x),y])^2](/vblatex/img/3c908a14ba1a267ae960f1c620d6e877-1.gif "Var[E_{\text{val}}(g^-)] = \frac{1}{K} (P[g^-(x),y])^2") |
![Var[E_{\text{val}}(g^-)]](/vblatex/img/f3911b70f8590e60dcca7bf86033a7ad-1.gif "Var[E_{\text{val}}(g^-)]")
can be obtained from part (a) by computing
, which is the variance (over x) of the error that the hypothesis makes. For the specific error measure, the error is bounded between [0,1], so you can bound this variance.