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Showing papers by "Luca Oneto published in 2010"


Proceedings ArticleDOI
18 Jul 2010
TL;DR: It is shown in this paper that, in a small sample setting, when the dimensionality of the data is larger than the number of samples, a careful application of the MLT can outperform other methods in selecting the optimal hyperparameters of a SVM.
Abstract: A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing effective SVM model selection. This fact is supported by experience, because well-known hold-out methods like cross-validation, leave-one-out, and the bootstrap usually achieve better results than the ones derived from MLT. We show in this paper that, in a small sample setting, i.e. when the dimensionality of the data is larger than the number of samples, a careful application of the MLT can outperform other methods in selecting the optimal hyperparameters of a SVM.

78 citations