Q4. Suppose our system is learning to recognize puppies and kittens from 80x80 pixel RGB images. Let x be a vector storing the pixel intensity values of the three color components for all the pixels. (a) If we train a logistic regression system with only the terms x1 as features, how many features do we have? (b) If we train a logistic regression system with all the quadratic terms (x;x;) as features, how many features do you have?
Q4. Suppose our system is learning to recognize puppies and kittens from 80x80 pixel RGB images. Let x be a vector storing the pixel intensity values of the three color components for all the pixels. (a) If we train a logistic regression system with only the terms x1 as features, how many features do we have? (b) If we train a logistic regression system with all the quadratic terms (x;x;) as features, how many features do you have?
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Transcribed Image Text:Q4.
Suppose our system is learning to recognize puppies and kittens from 80x80 pixel
RGB images. Let x be a vector storing the pixel intensity values of the three color components for
all the pixels.
(a) If we train a logistic regression system with only the terms x as features, how many features
do we have?
(b) If we train a logistic regression system with all the quadratic terms (x;x;) as features, how
many features do you have?
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