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ML Fundamentals5 min read
The Kernel Trick in Support Vector Machines (SVMs)
For the longest time I believed Support Vector Machines were maximum-margin linear classifiers with a fancy name. I could recite the objective function, draw the margin and the support vectors, and still feel smug. How is it finding a hyperplane in 2D when no straight line can ever separate these spirals? That confusion ended with the simplest sentence in ML: We never actually go to the high-dimensional space, we only pretend we do.
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