  The use of multivariate classifiers has become commonplace in particle
physics. To enhance the performance, a series of classifiers is typically
trained; this is a technique known as boosting. This paper explores several
novel boosting methods that have been designed to produce a uniform selection
efficiency in a chosen multivariate space. Such algorithms have a wide range of
applications in particle physics, from producing uniform signal selection
efficiency across a Dalitz-plot to avoiding the creation of false signal peaks
in an invariant mass distribution when searching for new particles.
