There is one predictor x, and the response yy(xx = 0) = "oo" and yy(xx = ±1) = " + ". This data set is not linearly separable by a SVM hyperplane. The SVM uses kernel trick to transform the data to (usually) a higher-dimensional space where the data become linearly separable. Find a kernel that will do the trick and identify the support vectors