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The CRF is trained using a fast Maximum Margin approach.
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Finally, we improve the accuracy of the CRF by replacing the maximum likelihood training of [8] with a more discriminative and very efficient maximum-margin approach that improves on [23] by using the cutting plane algorithm of [3] over FastPD [24] based subproblem optimization.
The second objective function for optimal allocation of FCLs is making the buses exceeding fault current limits, or with insufficient fault current margin, approach the maximum margin I M as closely as possible.
Inspired by the maximum margin of SVM, A. Kocsor et al. [37] propose the margin maximizing discriminant analysis (MMDA) approach.
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).
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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