PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Low cost estimation of $\sigma$ for SVM using local features
Vanessa Gomez-Verdejo, Miguel Lazaro-Gredilla and Emilio Parrado-Hernandez
(2008) Technical Report. Unpublished, Madrid, Spain.

Abstract

We investigate low cost methods to select the spread parameter in RBF kernels for Support Vector Machines. These methods try to gain information about the local structure of the dataset from the performance of simple local methods such as k-nearest neighbors. Empirical results in UCI datasets show that the proposed methods can be used as an alternative to the standard crossvalidation with the advantage that one does not need to fix a range of values of the parameter.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4914
Deposited By:Emilio Parrado-Hernandez
Deposited On:24 March 2009