Multi-criteria optimisation for complex learning prediction systems
This work presents a framework for the inclusion of multiple criteria in the design process of supervised learning algorithms; as well as studies the sophisticated interactions among them. The criteria included and tested experimentally in this thesis are: accuracy, model complexity, algorithmic com...
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Bournemouth University
2018
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| Online Access: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753148 |