Suppose a hard margin linear SVM classifier has been trained on a linearly separable 2D data and a horizontal decision boundary WTX + b = 0 has been found that passes through the origin. Weight vector W has 3 elements w0, w1, w2 such that w0 + w1x1 + w2x2 = 0 Which set of values is a correct assignment to the weight vector to characterize the decision boundary of this SVM classifier? w0=1 w1=0 w2=-1 w0=0 w1=1 w2=-1 w0=1 w1=-1 w2=1 w0=0 w1=0 w2=1

for this case we have by definition, if two lines are perpendicular, then the product of their slopes is -1.

then, given the following line:

so,

we are looking for m_ {2}:

then, the line is given by:

we find "b" replacing the given point:

finally, the equation is:

option c

there are 70 more legs than eyes at the dog show (196 – 126 = 70). dogs have two more legs than eyes, and humans have the same number of legs as eyes. thus 70 is twice the number of dogs at the show, so the number of dogs is half of 70, which is 35. to get the number of humans, subtract 35 from 63: 63 – 35 = 28.

step-by-step explanation: