  The use of multivariate classifiers, especially neural networks and decision
trees, has become commonplace in particle physics. Typically, a series of
classifiers is trained rather than just one to enhance the performance; this is
known as boosting. This paper presents a novel method of boosting that produces
a uniform selection efficiency in a user-defined multivariate space. Such a
technique is ideally suited for amplitude analyses or other situations where
optimizing a single integrated figure of merit is not what is desired.
