Ho-Kashyap with Early Stopping Versus Soft Margin SVM for Linear Classifiers - An Application
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چکیده
In a classification problem, hard margin SVMs tend to minimize the generalization error by maximizing the margin. Regularization is obtained with soft margin SVMs which improve performances by relaxing the constraints on the margin maximization. This article shows that comparable performances can be obtained in the linearly separable case with the Ho–Kashyap learning rule associated to early stopping methods. These methods are applied on a non-destructive control application for a 4-class problem of rail defect classification.
منابع مشابه
Ho–Kashyap with Early Stopping vs Soft Margin SVM for Linear Classifiers – An Application
In a classification problem, hard margin SVMs tend to minimize the generalization error by maximizing the margin. Regularization is obtained with soft margin SVMs which improve performances by relaxing the constraints on the margin maximization. This article shows that comparable performances can be obtained in the linearly separable case with the Ho–Kashyap learning rule associated to early st...
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