  An event reweighting technique incorporated in multivariate training
algorithm has been developed and tested using the Artificial Neural Networks
(ANN) and Boosted Decision Trees (BDT). The event reweighting training are
compared to that of the conventional equal event weighting based on the ANN and
the BDT performance. The comparison is performed in the context of the physics
analysis of the ATLAS experiment at the Large Hadron Collider (LHC), which will
explore the fundamental nature of matter and the basic forces that shape our
universe. We demonstrate that the event reweighting technique provides an
unbiased method of multivariate training for event pattern recognition.
