Name
Title | ||
|---|---|---|
Support Vector Machines And Strictly Positive Definite Kernel: The Regularization Hyperparameter Is More Important Than The Kernel Hyperparameters |
Abstract | ||
|---|---|---|
When dealing with a Support Vector Machine (SVM) with a strictly positive definite kernel, a common misconception is that the main handle for controlling the nonlinearity of the classification surface is the set of kernel hyperparameters. We show here that this is not the case: in particular, we prove that, regardless of the value of the kernel hyperparameter, it is always possible to tune the nonlinearity of the classifier by acting only on the regularization hyperparameter C, even achieving perfect learning of any non-degenerate training set. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Hyperparameter optimization,Radial basis function kernel,Hyperparameter,Pattern recognition,Kernel embedding of distributions,Computer science,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning,Regularization perspectives on support vector machines |
DocType | ISSN | Citations |
Conference | 2161-4393 | 2 |
PageRank | References | Authors |
0.37 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Oneto | 1 | 830 | 63.22 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Sandro Ridella | 3 | 677 | 140.62 |
| Davide Anguita | 4 | 1001 | 70.58 |