Suppose you are given $6$ one-dimensional points: $3$ with negative labels $x_1 = −1$, $x_2 = 0$, $x_3 = 1$ and $3$ with positive labels $x_4 = −3$, $x_5 = −2$, $x_6 = 3$. In this question, we first
compare the performance of linear classifier with or without kernel. Then we solve for
the maximum margin classifier using SVM.
Consider a linear classifier of form $f(x) = sign(w_1x+w_0)$. Write down the optimal value of $w$ and its classification accuracy on the above 6 points. There might be more than one optimal solution, writing down one of them is enough.
I understand that the data isn't linearly separable and that there will be some error, but I don't get how to get the optimal value of $w$. Do I minimize $f(x)$? But how do I take the derivative of $f(x)$? Any guidance would be appreciated, I'm a little lost.