Constraints in Weighted Averaging

9 years 6 months ago
Constraints in Weighted Averaging
Weighted averaging of classifier outputs is used in many MCSs, yet is still not well understood. Several empirical studies have investigated the effect that non-negativity and sum-one constraints have on the error rate of weighted averaging rules, but there is little theory available to understand the results. In this paper we study how constraints on the weights affect the location of the decision boundary of a MCS using weighted averaging. This allows us to explain many of the empirical findings, and suggest guidelines for when the application of constraints may or may not be appropriate. We also consider how these results relate to the analytical framework first proposed by Tumer and Ghosh [5].
Amber Tomas
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where MCS
Authors Amber Tomas
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