Mathematics, 19.03.2020 03:03 Brittpaulina
In these cases, we might try to correct for noise while training the classifier. Consider the following formulation for training a logistic regression classifier w ∈ R d on a noisy training data set (x (1), y(1)), . . . ,(x (n) , y(n) ), where for each i, y (i) ∈ {−1, +1}. For simplicity, we ignore the bias term b. Suppose we know that the noise magnitude is at most r. Then, instead of the standard logistic regression loss, we might want to minimize the following loss: L˜(w) = Pn i=1 L˜ i(w), where, L˜ i(w) = max z (i):kz (i)−x(i)k≤r log(1 + exp(−y (i)w >z (i) )), where kvk means the L2-norm of vector v. (a) (5 points) Prove that for all i, L˜ i(w) = Mi(w), where Mi(w) = log(1 + exp(rkwk − y (i)w >x (i) )). For full credit, show all the steps in your proof.
Answers: 2
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Adriana sold 50 shares of a company’s stock through a broker. the price per share on that day was $22.98. the broker charged her a 0.75% commission. what was adriana’s real return after deducting the broker’s commission? a. $8.62 b. $229.80 c. $1,140.38 d. $1,149.00
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Complex numbers multiply √-4 * √-25 and show all intermediate steps. alternative notation is sqrt(-4) * sqrt(-25).
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Mathematics, 21.06.2019 20:20
20 solve 2(4 x + 3) < 5 x + 21. a) { x | x < 9} b) { x | x > -5} c) { x | x > -9} d) { x | x < 5}
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In these cases, we might try to correct for noise while training the classifier. Consider the follow...
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