Optimizing the Classification Cost using SVMs with a Double Hinge Loss

Abstract

The objective of this study is to minimize the classification cost using Support Vector Machines (SVMs) Classifier with a double hinge loss. Such binary classifiers have the option to reject observations when the cost of rejection is lower than that of misclassification. To train this classifier, the standard SVM optimization problem was modified by… (More)

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