Physics, 20.04.2021 23:00, hmontalvo22
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
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There is one predictor x, and the response yy(xx = 0) = "oo" and yy(xx = ±1) = " + ". This data set...
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