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There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. So we choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If
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is a classifier that explicitly utilizes the margin of each example while learning a
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of a single data point is defined to be the distance from the data point to a
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may be too technical for most readers to understand
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