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This is because max margin methods, such as SVMs, tend to produce scores distributed around the − 1 to 1 range.
As we all know, the all existing structural large margin methods are the common in considering all structural information within classes into one model.
In our experiment, we apply two important classes of methods, namely linear large margin methods (represented by the linear support vector machine, SVM) and ensembles of decision trees (Random Forests, RF).
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Then, the critical margin method is applied in each set.
We have combined Monte-Carlo method with the critical margin method.
A Bayesian statistical framework is presented for Zimmerman and Weissenburger flutter margin method which considers the uncertainties in aeroelastic modal parameters.
The usefulness of the Bayesian flutter margin method is demonstrated using synthetic data generated from a two-degree-of-freedom pitch-plunge aeroelastic model.
Proposed HHO is based on using hysteresis margin method [22] in addition to DT which is calculated based on inverse of velocity estimates.
By using a simple Bode plot technique and a method known as the phase margin method, the relative stability of a feedback system can be determined.
Adopting weighted margin method, the foundational control equations for calculating the temperature field are derived, and two types of thermal boundary conditions are tackled.
As the data is not always separable linearly, the soft margin method is proposed as a modified maximum margin idea that allows for mislabeled examples, i.e., it allows some of the training set data points to be misclassified.
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