Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method

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.

• Type: Conference
• Year: 2009
• Where: MLDM
• Authors: Roberto Tronci, Giorgio Giacinto, Fabio Roli
• Keywords: Linear Combinations, Machine Learning, MLDM 2009, Trained Combination Rules, Two-class Score-based Problems