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Svm model agency

Svm model agency

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Svm model agency

Svm model agency

Svm model agency

Svm model agency

Svm model agency

Svm model agency. Content / Usage Questions

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Email Address. Sign In. Access provided by: anon Sign Out. However, conventional SVM only considers the maximization of the margin but ignores the minimization of the radius, which restricts its performance when applied to joint learning of feature transformation and the SVM classifier.

In this paper, we suggest a novel approximation for the radius of the minimum enclosing ball in feature space, and then propose a convex radius-margin-based SVM model for joint learning of feature transformation and the SVM classifier, i. A generalized block coordinate descent method is adopted to solve the F-SVM model, where the feature transformation is updated via the gradient descent and the classifier is updated by employing the existing SVM solver.

By incorporating with kernel principal component analysis, F-SVM is further extended for joint learning of nonlinear transformation and the classifier. F-SVM can also be incorporated with deep convolutional networks to improve image classification performance.

Article :. Date of Publication: 05 February DOI: Need Help?

Svm model agency

Svm model agency

Svm model agency