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Exact(30)
The hyperplane maximizes the margin between these support vectors.
The SVM finds a hyperplane h(x) which maximizes the margin.
This optimal separating hyperplane (OSH) w: wx + b = 0 maximizes the margin of the closest data points.
The hyperplane that maximizes the margin (i.e., the distance from the nearest training points) is selected by the SVM.
The quantity | | w | | 2 is named the margin, and optimal separating hyperplane (OSH) is the separating hyperplane which maximizes the margin.
For application to classification, SVMs divide a training dataset with a hyperplane that maximizes the margin between two classes in the first step.
Similar(30)
SVM sets the separation plane to maximize the margin.
Therefore, the SVM has to simultaneously maximize the margin and minimize the error.
SVM finds an optimal value of W to maximize the margin.
SVM learns the parameters required to maximize the margin from training sample data.
C is a regularization parameter that controls the trade-off between maximizing the margin and minimizing the training error.
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