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• We have the class of population distributions t ~ p(t) = N(tu, 1), where u is a real number. (There is no input x in this problem.) The model class H is given by the constant predictors Ê R, and the ₂ loss is adopted. (a) Obtain the expression of the ERM predictor RM for any training data set D = {(tn)_1}. (b) Write the expression of the estimation error as a function of μ and ERM. Note that the estimation error is a rv due to the dependence on the training data.

• We have the class of population distributions t ~ p(t) = N(tu, 1), where u is a real number. (There is no input x in this problem.) The model class H is given by the constant predictors Ê R, and the ₂ loss is adopted. (a) Obtain the expression of the ERM predictor RM for any training data set D = {(tn)_1}. (b) Write the expression of the estimation error as a function of μ and ERM. Note that the estimation error is a rv due to the dependence on the training data.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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• We have the class of population distributions t ~ p(t) = N(tu, 1), where u is a real number. (There is no input x in this problem.) The model class H is given by the constant predictors Ê R, and the ₂ loss is adopted. (a) Obtain the expression of the ERM predictor RM for any training data set D = {(tn)_1}. (b) Write the expression of the estimation error as a function of μ and ERM. Note that the estimation error is a rv due to the dependence on the training data.
Transcribed Image Text:• We have the class of population distributions t ~ p(t) = N(tu, 1), where u is a real number. (There is no input x in this problem.) The model class H is given by the constant predictors Ê R, and the ₂ loss is adopted. (a) Obtain the expression of the ERM predictor RM for any training data set D = {(tn)_1}. (b) Write the expression of the estimation error as a function of μ and ERM. Note that the estimation error is a rv due to the dependence on the training data.
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