  This paper compares the performance of artificial neural networks and boosted
decision trees, with and without cascade training, for tagging b-jets in a
collider experiment. It is shown, using a Monte Carlo simulation of $WH \to
l\nu q\bar{q}$ events, that for a b-tagging efficiency of 50%, the light jet
rejection power given by boosted decision trees without cascade training is
about 55% higher than that given by artificial neural networks. The cascade
training technique can improve the performance of boosted decision trees and
artificial neural networks at this b-tagging efficiency level by about 35% and
80% respectively. We conclude that the cascade trained boosted decision trees
method is the most promising technique for tagging heavy flavours at collider
experiments.
