Sentence examples for margin criterion from inspiring English sources
maximum margin criterion.
Based on the similar idea, H. F. Li et al. [38] present the maximum margin criterion (MMC) method.
The comparison of the IPLS was performed with incremental PCA algorithm, incremental inter-class scatter method, and incremental maximum margin criterion technique.
In practice, there exist three corresponding well-known models, including the Locality Preserving Projection (LPP), the Linear Discriminant Analysis (LDA), and the Maximum Margin Criterion (MMC).
Second, ASFDA seeks projections to minimize within-class temporal variation and maximize between-class temporal variation simultaneously based on maximum margin criterion.
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.
In this way, the analysis of torque permits the selection of an optimal gait based on stability margin criteria.
Then, these parameters are used to design a two degree of freedom proportional-integral proportional-derivative (proportional-integral proportional-derivativegin and gain margin criteria.
Therefore, the algorithm only uses the OVA and the biggest margin criteria for performing an oblique binary split PSVM while dealing with (c le 3) (i.e. the plane (P_1)) as illustrated on the right-hand-side of Fig. 3. Open image in new window Fig. 3 Oblique splitting for (c) classes ((c le 3)).
examples of the supervised methods are maximum margin criteria (MMC), maximum relevance minimum redundancy (mRMR) and linear discriminate analysis (LDA), (ii) the unsupervised technique, however, transform the existing features by rotating and projecting them onto a minimal number of axes without using the target labels.
This study introduces new ensemble margin criteria to evaluate the performance of Random Forests (RF) in the context of large area land cover classification and examines the effect of different training data characteristics (imbalance and mislabelling) on classification accuracy and uncertainty.
