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EnglishA 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.
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| Titolo: | Model Selection for Support Vector Machines: Advantages and Disadvantages of the Machine Learning Theory |
| Autori: | |
| Data di pubblicazione: | 2010 |
| 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. |
| Handle: | http://hdl.handle.net/11567/315097 |
| ISBN: | 9781424469161 |
| Appare nelle tipologie: | 04.01 - Contributo in atti di convegno |
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