  We study classification problems where features are corrupted by noise and
where the magnitude of the noise in each feature is influenced by the resources
allocated to its acquisition. This is the case, for example, when multiple
sensors share a common resource (power, bandwidth, attention, etc.). We develop
a method for computing the optimal resource allocation for a variety of
scenarios and derive theoretical bounds concerning the benefit that may arise
by non-uniform allocation. We further demonstrate the effectiveness of the
developed method in simulations.
