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Thus the maximum margin solution is found by solving (5).
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To find the optimal solution for each local hyperplane, maximum margin technique is used to minimize the generalization error in the smaller but more relevant subset of the training data.
Since there could exist more than one solution to this separation problem, SVM searches the only H with the maximum margin, which means that the distance or separation of the two classes are maximized by the choice of H.
The poll has a maximum margin of sampling error of plus or minus three points.
maximum margin criterion.
orthogonal maximum margin projection subspace.
The maximum margin is around 21%%.
orthogonal kernel maximum margin projection subspace.
Although there are infinite number of solutions to separate hyperplanes, by maximizing the margin, Figure 1 shows only two decision functions satisfying (4). Figure 1 Maximum margin of optimal hyperplane.
The CRF is trained using a fast Maximum Margin approach.
Inspired by the maximum margin of SVM, A. Kocsor et al. [37] propose the margin maximizing discriminant analysis (MMDA) approach.
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