Decision Boundaries and Kernel Function Assignment Answer

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Assignment Help Answer With Step-by-Step Explanation:

For the negative class (-):

w0 + w1 * x1 + w2 * x2 < 0

w0 + 2w2 > 0

2. For a negative example (1,0,-):

-3w1 + 2w2 > -w0

4. For a negative example (0,-4,-):

w0 + w1 + 5w2 > 0

6. For a negative example (-1,-6,-):

5w1 + w2 < -w0

8. For a positive example (0,4,+):

-2w1 > -w0

Now, we have a system of linear equations involving w0, w1, and w2. We can solve this system to find the equation of the decision boundaries and estimate the width of the decision boundary.

Expanding the above expression:

κ(U, V) = (U·V)^2 + 2(U·V) + 1

c) Given the feature vectors X1(1,1) and X2(0,1), we can compute their corresponding feature vectors in the new feature space using the function φ(X) obtained in part b:

For X1(1,1):