Efficient optimal linear boosting of a pair of classifiers.

Boosting is a meta-learning algorithm which takes as input a set of classifiers and combines these classifiers to obtain a better classifier. We consider the combinatorial problem of efficiently and optimally boosting a pair of classifiers by reducing this problem to that of constructing the optimal linear separator for two sets of points in two dimensions. Specifically, let each point x element of R be assigned a weight W(x) > 0, where the weighting function can be an arbitrary positive function.