Trace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line in 4-dimensional space passing through the origin with normal vector w* = (-1, 2, -3, 2); for example, the true label of (1, 1, 0, 1) is '+1' and that of (0, 0, 1, 1) is '-1' Assume the current weight function at time t is w₁ = (2, 1, -1, 1), and the next point is x₁ = (1, 0, 2, 2). What is the updated weight function w++ 1 at the end of round t? You must follow the same assumptions as in the example of Module 10. In particular, you need to normalize. the example. a. None of the other answers are correct. O b. (7/3,1,-1/3,5/3) ○ c. (-7/3, 1, -5/3, 1/3) d. (-5/3, 1, -1/3, 5/3) e. (5/3, 1, -5/3, 1/3) f. (-2, 4/3, -5/3, 1/3) g. (-2, 1, −1, 1) Oh. (-2, 2/3, -1/3, 5/3)
Trace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line in 4-dimensional space passing through the origin with normal vector w* = (-1, 2, -3, 2); for example, the true label of (1, 1, 0, 1) is '+1' and that of (0, 0, 1, 1) is '-1' Assume the current weight function at time t is w₁ = (2, 1, -1, 1), and the next point is x₁ = (1, 0, 2, 2). What is the updated weight function w++ 1 at the end of round t? You must follow the same assumptions as in the example of Module 10. In particular, you need to normalize. the example. a. None of the other answers are correct. O b. (7/3,1,-1/3,5/3) ○ c. (-7/3, 1, -5/3, 1/3) d. (-5/3, 1, -1/3, 5/3) e. (5/3, 1, -5/3, 1/3) f. (-2, 4/3, -5/3, 1/3) g. (-2, 1, −1, 1) Oh. (-2, 2/3, -1/3, 5/3)
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