In two-class score-based problems the combination of scores from an ensemble of experts is generally used to obtain distributions for positive and negative patterns that exhibit a larger degree of separation than those of the scores to be combined. Typically, combination is carried out by a “static” linear combination of scores, where the weights are computed by maximising a performance function. These weights are equal for all the patterns, as they are assigned to each of the expert to be combined. In this paper we propose a “dynamic” formulation where the weights are computed individually for each pattern. Reported results on a biometric dataset show the effectiveness of the proposed combination methodology with respect to “static” linear combinations and trained combination rules.

Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method / Tronci R; Giacinto G; Roli F. - 5632(2009), pp. 163-177. ((Intervento presentato al convegno Machine Learning and Data Mining in Pattern Recognition (MLDM 2009) nel 2009.

Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method

TRONCI, ROBERTO;GIACINTO, GIORGIO;ROLI, FABIO
2009

Abstract

In two-class score-based problems the combination of scores from an ensemble of experts is generally used to obtain distributions for positive and negative patterns that exhibit a larger degree of separation than those of the scores to be combined. Typically, combination is carried out by a “static” linear combination of scores, where the weights are computed by maximising a performance function. These weights are equal for all the patterns, as they are assigned to each of the expert to be combined. In this paper we propose a “dynamic” formulation where the weights are computed individually for each pattern. Reported results on a biometric dataset show the effectiveness of the proposed combination methodology with respect to “static” linear combinations and trained combination rules.
978-3-642-03069-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/103928
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