Specify the parameters of Lipschitzness and smoothness.

1. Construct an example showing that the 0−1 loss function may suffer from local minima; namely, construct a training sample ∈ (×{±1})(say, for = R2), for which there exist a vector w and some _ >0 such that

2. For any w_ such that                 w_  ≤we have LS(w) ≤ LS (w_) (where the loss here is the 0−1 loss). This means that w is a local minimum of LS .

3. There exists some w∗ such that LS(w∗) <>LS(w). This means that w is not a global minimum of LS .

4. Consider the learning problemof logistic regression: LetH=={x∈R:                 x              ≤ B}, for some scalar 0, let = {±1}, and let the loss function      be defined a (w(xy)) = log(1 + exp ( − y_w,x_)). Show that the resulting learning problem is both convex-Lipschitz-bounded and convex-smooth-bounded. Specify the parameters of Lipschitzness and smoothness.


find the cost of your paper

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