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Therefore, the maximum margin term is quite important and should be taken into consideration.
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Finally, the maximum margin classifier is calculated by solving the following constrained optimization problem which is expressed in terms of variables α i : maximize α ∑ i = 1 n α i − 1 2 ∑ i = 1 n ∑ j = 1 n y i y j α i α j x i T x j subject to : ∑ i = 1 n y j α i = 0, 0 ≤ α i ≤ C. The constant C > 0 defines the trade-off between the training error and the margin.
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.
Thus the maximum margin solution is found by solving (5).
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.
Based on the objective function and the maximum margin criterion, a dimensionality reduction algorithm, the non-negative sparse semi-supervised maximum margin algorithm, is proposed.
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