Name
Title | ||
|---|---|---|
Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596450 | Neural Networks |
Keywords | Field | DocType |
learning (artificial intelligence),support vector machines,MLT,SVM model selection,machine learning theory,optimal hyperparameters selection,support vector machines | Structured support vector machine,Online machine learning,Active learning (machine learning),Pattern recognition,Computer science,Support vector machine,Model selection,Curse of dimensionality,Artificial intelligence,Computational learning theory,Relevance vector machine,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 12 |
PageRank | References | Authors |
0.83 | 41 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D. Anguita | 1 | 25 | 1.77 |
| Alessandro Ghio | 2 | 13 | 1.17 |
| Greco, N. | 3 | 12 | 0.83 |
| Luca Oneto | 4 | 830 | 63.22 |