3. Let yi € {−1,1} and f(x) = ßo + ¹x denote a linear classification function. (a) (LR) Show that maximizing (1) is equivalent to minimize = [[1+e=vif(x)] n (b) (SVM) Show that (2) is equivalent to minimize 1 11- i=1 [1 − Yif (xi)] ++. BT B where f(x) = Bo + BTx. (c) Plot the two loss functions obtained from (a) and (b) as a function of margin in order to check the similarity between LR and SVM.
3. Let yi € {−1,1} and f(x) = ßo + ¹x denote a linear classification function. (a) (LR) Show that maximizing (1) is equivalent to minimize = [[1+e=vif(x)] n (b) (SVM) Show that (2) is equivalent to minimize 1 11- i=1 [1 − Yif (xi)] ++. BT B where f(x) = Bo + BTx. (c) Plot the two loss functions obtained from (a) and (b) as a function of margin in order to check the similarity between LR and SVM.
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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Please no written by hand solution and no img
![3. Let y₁ € {−1,1} and f(x) = ßo + ß¹x denote a linear classification function.
(a) (LR) Show that maximizing (1) is equivalent to minimize
1
n
n
(b) (SVM) Show that (2) is equivalent to minimize
n
Σ[1 + e−1+5(x;)]
i=1
[1-yif (xi)] ++
X
B
where f(x) = ßo + B¹x.
(c) Plot the two loss functions obtained from (a) and (b) as a function of margin in order to check the
similarity between LR and SVM.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F495ad580-3e5b-41d4-8f15-efaf901c5b2d%2F06bfe947-43d7-4d18-af1e-e550b581d906%2Frg1p78_processed.png&w=3840&q=75)
Transcribed Image Text:3. Let y₁ € {−1,1} and f(x) = ßo + ß¹x denote a linear classification function.
(a) (LR) Show that maximizing (1) is equivalent to minimize
1
n
n
(b) (SVM) Show that (2) is equivalent to minimize
n
Σ[1 + e−1+5(x;)]
i=1
[1-yif (xi)] ++
X
B
where f(x) = ßo + B¹x.
(c) Plot the two loss functions obtained from (a) and (b) as a function of margin in order to check the
similarity between LR and SVM.
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