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916 B
ReStructuredText
21 lines
916 B
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Classification
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Classifying data is a common task in machine learning. Suppose some
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given data points each belong to one of two classes, and the goal is
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to decide which class a new data point will be in.
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In the case of support vector machines, a data point is viewed as a
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p-dimensional vector (a list of p numbers), and we want to know
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whether we can separate such points with a p − 1-dimensional
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hyperplane. This is called a linear classifier. There are many
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hyperplanes that might classify the data. One reasonable choice as the
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best hyperplane is the one that represents the largest separation, or
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margin, between the two classes. So we choose the hyperplane so that
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the distance from it to the nearest data point on each side is
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maximized. If such a hyperplane exists, it is known as the
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maximum-margin hyperplane and the linear classifier it defines is
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known as a maximum margin classifier.
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