Loading [a11y]/accessibility-menu.js
Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters | IEEE Conference Publication | IEEE Xplore

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...Show More

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.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
ISBN Information:

ISSN Information:

Conference Location: Killarney, Ireland

Contact IEEE to Subscribe

References

References is not available for this document.