Support vector machines are universally consistent
Journal of Complexity
The Journal of Machine Learning Research
Sparseness of support vector machines
The Journal of Machine Learning Research
Some Properties of Regularized Kernel Methods
The Journal of Machine Learning Research
Bouligand Derivatives and Robustness of Support Vector Machines for Regression
The Journal of Machine Learning Research
Support Vector Machines
Consistency of support vector machines and other regularized kernel classifiers
IEEE Transactions on Information Theory
Consistency of support vector machines using additive kernels for additive models
Computational Statistics & Data Analysis
Asymptotic normality of support vector machine variants and other regularized kernel methods
Journal of Multivariate Analysis
Universal consistency of localized versions of regularized kernel methods
The Journal of Machine Learning Research
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Support vector machines (SVMs) have attracted much attention in theoretical and in applied statistics. The main topics of recent interest are consistency, learning rates and robustness. We address the open problem whether SVMs are qualitatively robust. Our results show that SVMs are qualitatively robust for any fixed regularization parameter @l. However, under extremely mild conditions on the SVM, it turns out that SVMs are not qualitatively robust any more for any null sequence @l"n, which are the classical sequences needed to obtain universal consistency. This lack of qualitative robustness is of a rather theoretical nature because we show that, in any case, SVMs fulfill a finite sample qualitative robustness property. For a fixed regularization parameter, SVMs can be represented by a functional on the set of all probability measures. Qualitative robustness is proven by showing that this functional is continuous with respect to the topology generated by weak convergence of probability measures. Combined with the existence and uniqueness of SVMs, our results show that SVMs are the solutions of a well-posed mathematical problem in Hadamard's sense.