On Hypothesis Discrimination with Loss Functions That Depend on Decisions

The authors consider the problem of hypothesis discrimination with concave loss functions of an error matrix. It is shown that decision functions in this case minimize the average risk for some constant loss matrix. A relationship that defines the matrices is given. The results are in a problem involving minimization of average information loss for a specified a priori distribution.