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http://stat-or.unc.edu/files/2016/04/07_11.pdf
Speciflcally, the robust truncated-hinge-loss support vector machine (RSVM) is very robust to outliers in the training data. Consequently, it can deliver higher classiflcation
http://compgen.unc.edu/ICASG/publications/RLeeSVM.pdf
On Multicategory Truncated-Hinge-Loss Support Vector Machines Yichao Wu and Yufeng Liu Abstract. With its elegant margin theory and accurate classification perfor-mance, the Support Vector Machine (SVM) has been widely applied in both machine learning and statistics. Despite its success and popularity, it still has some drawbacks in certain situations.
https://www.researchgate.net/publication/4742783_Robust_Truncated_Hinge_Loss_Support_Vector_Machines
Besides the robustness and smoothness, another nice property of RSVC lies in the fact that its solution can be obtained by solving weighted squared hinge loss-based support vector machine problems ...
https://stats.stackexchange.com/questions/74499/what-is-the-loss-function-of-hard-margin-svm
People says soft margin SVM use hinge loss function: $\max(0,1-y_i(w^\intercal x_i+b))$. ... ^2$ is the loss function in this case, can we call it quadratic loss function? If so, why the loss function of hard margin SVM becomes regularizer in soft margin SVM and make a change from quadratic loss to hinge loss? ... Support vector machine margin ...
https://www.cs.utah.edu/~piyush/teaching/13-9-print.pdf
Support Vector Machines (Contd.), Classification Loss Functions and Regularizers Piyush Rai CS5350/6350: Machine Learning September 13, 2011 (CS5350/6350) SVMs, Loss Functions and Regularization September 13, 2011 1 / 18
https://stackoverflow.com/questions/34325759/whats-the-relationship-between-an-svm-and-hinge-loss
Once you introduce kernel, due to hinge loss, SVM solution can be obtained efficiently, and support vectors are the only samples remembered from the training set, thus building a non-linear decision boundary with the subset of the training data. What about the slack variables?
https://cvstuff.wordpress.com/2014/11/29/latex-l_1-versus-latex-l_2-loss-a-svm-example/
Nov 29, 2014 · Recall the formula of Support Vector Machines whose solution is global optimum obtained from an energy expression trading off between the generalization of the classifier versus the loss incured when misclassifies some points of a training set , i.e.,. Here is the regularization coefficient and is any loss function. Popular choices of consist of Hinge loss, i.e., , and squared loss, i.e., .
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