Skip navigation
Skip navigation

Machine Learning using Hyperkernels

Ong, Cheng Song; Smola, Alexander


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
Source: Proceedings of the Twentieth International Conference on Machine Learning


File Description SizeFormat Image
01_Ong_Machine_Learning_using_2003.pdf1.08 MBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator