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|Title: ||A Kernel path algorithm for support vector machines|
|Authors: ||Wang, Gang|
Lochovsky, Frederick H.
|Keywords: ||Kernel function|
Support vector machines
|Issue Date: ||2007 |
|Citation: ||Proceedings 24th International Conference on Machine Learning (ICML 2007), Oregon, U.S., 20-24 June 2007, p. 951-958|
|Abstract: ||The choice of the kernel function which determines the mapping between the input space and the feature space is of crucial importance to kernel methods. The past few years have seen many efforts in learning either the kernel function or the kernel matrix. In this paper, we address this model selection issue by learning the hyperparameter of the kernel function for a support vector machine (SVM). We trace the solution path with respect to the kernel hyperparameter without having to train the model multiple times. Given a kernel hyperparameter value and the optimal solution obtained for that value, we find that the solutions of the neighborhood hyperparameters can be calculated exactly. However, the solution path does not exhibit piecewise linearity and extends nonlinearly. As a result, the breakpoints cannot be computed in advance. We propose a method to approximate the breakpoints. Our method is both efficient and general in the sense that it can be applied to many kernel functions in common use.|
|Rights: ||© ACM, 2007. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM international conference proceeding series, v. 227, p. 951-958|
|Appears in Collections:||CSE Conference Papers|
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