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Machine Learning using Hyperkernels

Ong, Cheng Song; Smola, Alexander

Description

We expand on the problem of learning a kernel via a RKHS on the space of kernels itself. The resulting optimization problem is shown to have a semidefinite programming solution. We demonstrate that it is possible to learn the kernel for various formulations of machine learning problems. Specifically, we provide mathematical programming formulations and experimental results for the C-SVM, v-SVM and Lagrangian SVM for classification on UCI data, and novelty detection.

CollectionsANU Research Publications
Date published: 2003
Type: Conference paper
URI: http://hdl.handle.net/1885/87995
Source: Proceedings of the Twentieth International Conference on Machine Learning

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