Fitting an SVM classifier by hand (Source: Jaakkola.) Consider a dataset with 2 point
Consider mapping each point to 3d using the feature vector
using a second order polynomial kernel.) The max margin classifier has the form
a. Write down a vector that is parallel to the optimal vector w. Hint: recall from Figure 7.8 (12Apr10
version) that w is perpendicular to the decision boundary between the two points in the 3d feature
space.
b. What is the value of the margin that is achieved by this w? Hint: recall that the margin is the distance
from each support vector to the decision boundary. Hint 2: think about the geometry of 2 points in
space, with a line separating one from the other.
c. Solve for w, using the fact the margin is equal to 1/||w||.
d. Solve for w0 using your value for w and Equations 14.97 to XXXXXXXXXXHint: the points will be on the
decision boundary, so the inequalities will be tight.
e. Write down the form of the discriminant function f(x) = w0 + wT φ(x) as an explicit function of x
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