Title: Minimum classification error training for Choquet integrals with applications to landmine detection

Speaker: Andres Mendez-Vazquez

Time: 3:00PM - 4:00 PM, Oct. 27th, 2006

Place: CSE 404

ABSTRACT:

A novel algorithm for discriminative training of Choquet integral based fusion operators is described. Fusion is performed by Choquet integration of classifier outputs with respect to fuzzy measures. The fusion operators are determined by the parameters of fuzzy measures. These parameters are found by minimizing a minimum classification error objective function. The minimization is performed with respect to a special class of measures, the Sugeno $\lambda$-measures. An analytic expression is derived for the gradient of the Choquet integral with respect to the Sugeno $\lambda$-measure. The new algorithm is applied to a landmine detection problem, and compared to previous techniques.