Given datasets X: x1 (1, 4), x2 (2, 3), x3 (3, 4), and x4 (5, -3), x5 (6, -1), x6 (7, -1) with label y = (- 1, -1, -1, 1, 1, 1) respectively, find analytically hard margin W and bias b so that you will be able to determine linear discriminant function f(X) = WT . X + b. You may need to draw the points and estimate the boundary decision function that may be used to help you to make a very first guess the parameter W and b. Once you found the correct discriminant function f(X) = WT . X + b, test your function to correctly classify x1 (1, 4) and x4 (5, -3). Please thoroughly describe.
Given datasets X: x1 (1, 4), x2 (2, 3), x3 (3, 4), and x4 (5, -3), x5 (6, -1), x6 (7, -1) with label y = (- 1, -1, -1, 1, 1, 1) respectively, find analytically hard margin W and bias b so that you will be able to determine linear discriminant function f(X) = WT . X + b. You may need to draw the points and estimate the boundary decision function that may be used to help you to make a very first guess the parameter W and b. Once you found the correct discriminant function f(X) = WT . X + b, test your function to correctly classify x1 (1, 4) and x4 (5, -3). Please thoroughly describe.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Given datasets X: x1 (1, 4), x2 (2, 3), x3 (3, 4), and x4 (5, -3), x5 (6, -1), x6 (7, -1) with label y = (- 1, -1, -1, 1, 1, 1) respectively, find analytically hard margin W and bias b so that you will be able to determine linear discriminant function f(X) = WT . X + b. You may need to draw the points and estimate the boundary decision function that may be used to help you to make a very first guess the parameter W and b. Once you found the correct discriminant function f(X) = WT . X + b, test your function to correctly classify x1 (1, 4) and x4 (5, -3). Please thoroughly describe.
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