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Found 4 papers, 0 papers with code
An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines
By exploring the special property of nonlinear SVMs, we transform the optimization problem of attacking nonlinear SVMs into a nonlinear KKT system.
Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines
In this paper, we investigate the optimization models and the interpretations for three types of adversarial perturbations against support vector machines, including sample-adversarial perturbations (sAP), class-universal adversarial perturbations (cuAP) as well as universal adversarial perturbations (uAP).
A Semismooth-Newton's-Method-Based Linearization and Approximation Approach for Kernel Support Vector Machines
Motivated by this question and the idea in linearization framework, in this paper, we focus on the $L_2$-loss kernel SVM and propose a semismooth Newton's method based linearization and approximation approach for it.
Ordinal Distance Metric Learning with MDS for Image Ranking
Image ranking is to rank images based on some known ranked images.
