Abstract

We propose to combine simple discriminators for ob ject dis- crimination under the maximum entropy framework or equivalently un- der the maximum likelihood framework for the exponential family. The duality between the maximum entropy framework and maximum likeli- hood framework allows us to relate two selection criteria for the discrimi- nators that were proposed in the literature. We illustrate our approach by combining nearest prototype discriminators that are simple to implement and widely applicable as they can be constructed in any feature space with a distance function. For eficient run-time performance we adapt the work on “alternating trees” for multi-class discrimination tasks. We report results on a multi-class discrimination task in which significant gains in performance are seen by combining discriminators under our framework from a variety of easy to construct feature spaces.