Segata, Nicola and Blanzieri, Enrico (2008) Operators for transforming kernels into quasi-local kernels that improve SVM accuracy. UNSPECIFIED. (Unpublished)

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Abstract

In the field of statistical machine learning, the integration of kernel methods with local information has been proposed through locality-improved kernels for Support Vector Machines (SVM) that make use of prior information, local kernels and local SVM that apply the SVM approach only on the subset of points close to the testing one. Here we propose a novel family of operators on kernels able to integrate the local information into any kernel without prior information obtaining quasi-local kernels. The quasi-local kernels maintain the possibly global properties of the input kernel and they increase the kernel value as the points get closer in the feature space of the input kernel. The operators combine the input kernel with a locality-dependent term, and accept two parameters that regulate the width of the exponential influence of points in the locality-dependent term and the balancing between the two terms. Experiments carried out with data-dependent systematic selection of the parameters of the operators (i.e. without the need for model selection phase on the obtained kernels) on a total of 33 datasets with different characteristics and application domains, achieve very good results.

Available Versions of this Item

• Operators for transforming kernels into quasi-local kernels that improve SVM accuracy. (deposited 14 Aug 2009)[Currently Displayed]
  • Operators for transforming kernels into quasi-local kernels that improve SVM accuracy. (deposited 14 Aug 2009)

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