This is now an inactive research group it's members have moved on. You can find them at their new research groups:
Research Projects

Research Projects

Delimiting kernel-based learning methods
(this project has ended)

Learning systems based on kernels are a powerful class of algorithms that includes Support Vector Machines and Gaussian Processes. These systems have become a major part of current research into and applications of adaptive systems. Despite this fact very little is known about when we can expect these systems to perform well. There has even been the assumption made that they provide a universal learning methodology. The proposed project will address this in order to:

  • provide theoretical tools that describe when a set of functions can be realised by hyperplanes with non-trivial margins in some feature space;
  • describe how the degree of matching between a kernel and a problem domain can be measured;
  • develop methods for choosing kernels as attuned as possible to a particular problem/domain;
  • develop alternative `luckiness' functions that give rise to efficient generic learning methods for problems that cannot be solved using kernel methods.

Type: Normal Research Project
Research Group: Information: Signals, Images, Systems Research Group
Theme: Machine Learning
Dates: 1st September 2002 to 31st August 2004

Funding

  • EPSRC

Principal Investigators

  • jst

Other Investigators

  • aa

Associated Publications

Welcome to ePrints Soton - ePrints Soton
The University of Southampton

Welcome to ePrints Soton

Welcome to the University of Southampton Institutional Research Repository, ePrints Soton. This repository contains details and, if available, downloads of our research output.

Information on this website should be updated via PURE, our research management system. For issues and queries on outputs and open access, please contact the ePrints team at eprints@soton.ac.uk or view the University's Pure support pages.

Search Repository

Search the repository using a full range of fields. Use the search field at the top of the page for a quick search.

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×
© School of Electronics and Computer Science of the University of Southampton