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University of Southampton policies regarding the ePrints Soton research repository.
This project is concerned with the development and application of optimisation methods for machine learning algorithms. Many modern machine learning algorithms can be viewed as optimising bounds on the generalisation error derived in learning theory. Modern tools from mathematical programming such as second-order cone and semi-definite programs will be adapted to the optimisation problems arising in machine learning. The resulting methods will be tested on benchmark data and - whenever possible - on suitable real-world data sets.
Type: Postgraduate ResearchWelcome 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.
University of Southampton policies regarding the ePrints Soton research repository.
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Contact ePrints Soton: eprints@soton.ac.uk
ePrints Soton supports OAI 2.0 with a base URL of http://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.