Prove that learning the class of logistic regressors hw Rd [0, 1] with loss function 1(hw,...

 

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Prove that learning the class of logistic regressors hw Rd [0, 1] with loss function 1(hw, (x, y)) = log(1+ exp(-y(w, x))); (x, y) Rd {1,1} via ERM is equivalent to Maximum Likelihood estimation of the parameters w = of the model ... wa) (w1, W2,... Dyx (yx) = hw(x) (y+1)/2 (1 hw(x)) (1-y)/2; Dx - arbitrary, for x Rd, y = {1,1} Prove that learning the class of logistic regressors hw Rd [0, 1] with loss function 1(hw, (x, y)) = log(1+ exp(-y(w, x))); (x, y) Rd {1,1} via ERM is equivalent to Maximum Likelihood estimation of the parameters w = of the model ... wa) (w1, W2,... Dyx (yx) = hw(x) (y+1)/2 (1 hw(x)) (1-y)/2; Dx - arbitrary, for x Rd, y = {1,1}