Sparseness of support vector machines
The Journal of Machine Learning Research
Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm
Neurocomputing
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
IEEE Transactions on Image Processing
Conjugate relation between loss functions and uncertainty sets in classification problems
The Journal of Machine Learning Research
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We determine the asymptotically optimal choice of the parameter ν for classifiers of ν-support vector machine (ν-SVM) type which has been introduced by Scholkopf et al. (2000). It turns out that ν should be a close upper estimate of twice the optimal Bayes risk provided that the classifier uses a so-called universal kernel such as the Gaussian RBF kernel. Moreover, several experiments show that this result can be used to implement some modified cross validation procedures which improve standard cross validation for ν-SVMs.