Classiication Properties of Support Vector Machines for Regression Classiication Properties of Support Vector Machines for Regression
نویسنده
چکیده
In this report we show some consequences of the work done by Pontil et al. in 1]. In particular we show that in the same hypotheses of the theorem proved in their paper, the optimal approximating hyperplane f R found by SVM regression classiies the data. This means that y i f R (x i) > 0 for points which live externally to the margin between the two classes or points which live internally to the margin but correctly classiied by SVM classiication. Moreover y i f R (x i) < 0 for incorrectly classiied points. Finally, the zero level curve of the optimal approximating hyperplane determined by SVMR and the optimal separating hyperplane determined by SVMC coincide.
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