Other publication
Kernel-Based Learning of Conditional Utility Functions
Authors: Pahikkala T
Publication year: 2013
Book title : European Conference on Operational Research (EURO 2013)
Web address : http://www.euro-online.org/conf/euro26/treat_abstract?paperid=16575
Abstract
In this work, we consider the problem of learning cardinal utility functions with a condition. The condition being, for example, a query object given at prediction time, the learned utility function assigns to it the cardinal utility values of a set of target objects, also given at prediction time. On one hand, we analyze the universal approximation properties of kernel-based regression algorithms on this task, and on the other hand, we study how the generalization properties of the algorithms can be improved via incorporation of certain invariances inherent to the considered task.
In this work, we consider the problem of learning cardinal utility functions with a condition. The condition being, for example, a query object given at prediction time, the learned utility function assigns to it the cardinal utility values of a set of target objects, also given at prediction time. On one hand, we analyze the universal approximation properties of kernel-based regression algorithms on this task, and on the other hand, we study how the generalization properties of the algorithms can be improved via incorporation of certain invariances inherent to the considered task.
UTU Research Portal