  We first present a general risk bound for ensembles that depends on the Lp
norm of the weighted combination of voters which can be selected from a
continuous set. We then propose a boosting method, called QuadBoost, which is
strongly supported by the general risk bound and has very simple rules for
assigning the voters' weights. Moreover, QuadBoost exhibits a rate of decrease
of its empirical error which is slightly faster than the one achieved by
AdaBoost. The experimental results confirm the expectation of the theory that
QuadBoost is a very efficient method for learning ensembles.
