This document describes training a hard-margin support vector machine (SVM) on a small dataset with 4 points from two classes. It asks to find the parameters for the linear hypothesis function that separates the classes with the maximum margin and to draw the resulting decision boundaries, margins, and support vectors.

1. You are training a hard-margin SVM on a tiny dataset with 4 points shown in the figure. This
dataset consists of two examples with class label -1 (denoted with triangles), and two examples
with class label +1 (denoted with plus).
given the hypothesis h(x) =
0
+
1
x
1
+
2
x
2
,
Q1: Please find the parameters
0
,
1
,
2
for this hard-margin SVM
Q2: Please draw the decision boundaries, the margins and support vectors.