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A support-vector machine is often a supervised learning product that divides the data into areas divided by a linear boundary. Here, the linear boundary divides the black circles in the white. Supervised learning algorithms build a mathematical product of a set of knowledge which contains both the inputs and the specified outputs.[47] The data, kno
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