A theoretical study on six classifier fusion strategies

We look at a single point in the feature space, two classes, and L classifiers estimating the posterior probability for class omega(1). Assuming that the estimates are independent and identically distributed (normal or uniform), we give formulas for the classification error for the following fusion methods: average, minimum, maximum, median, majority vote, and oracle.