Support Vector Machines and Kernel Methods
نویسنده
چکیده
Suppose we choose a group of data points, which could reasonably separate information regions. These data points that lie close to separation regions, selected among all the input data, are commonly called “support vectors”. Assume that we have group of data {xi, yi}that could be separated by a hyperplane. Thus we can write the following statements about the separating hyperplanes, { β.xi + β0 ≥ +1, if yi = +1 β.xi + β0 ≤ −1, if yi = −1. Equivalently we could write the above separating equations as follows:
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