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a Hinge loss.
Also, the function (H_1 cdot )=max (0,1-cdot )) is the Hinge Loss function.
These are found using standard SVM with hinge loss in both the linear and nonlinear cases.
where ℓ is a hinge loss function ℓ=max 0,ℓ n ).
The updating is done through a positive regularization parameter and a hinge loss function.
The regularizer is then combined with the large margin hinge loss on the triplet constraints.
The standard hinge loss function is not differentiable everywhere, but here we can use quadratic hinge loss as below instead to make use of gradient-based optimization methods, e.g., LBFGS [6].
P-CENI first adopts a heuristic method by using a hinge loss estimator to identify the critical dimension.
To train the classifier, a supervised learning algorithm is devised to minimize the hinge loss function under the new architecture.
Additionally, we apply the techniques developed to minimize an objective function with a truncated hinge loss function.
Use of misclassification information and hinge loss error in growing/learning criterion helps in approximating the decision function accurately.
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