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Thus, the derived trajectory is a physically constrained path inasmuch as it considers the maximum margin notion of the SVM theory.
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Such a model can be derived by building on the notion of the maximum margin matrix factorization [27].
Such a set of mappings is obtained, building on the notion of the maximum margin matrix factorization, by minimizing a weighted sum of nuclear norms.
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.
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