  For reactor neutrino experiments including the next--generation experiments
will be adopting the liquid scintillator technique, criteria and time to select
neutrino--induced inverse beta decay events from the background events need to
be established. For higher performance efficiency, we investigated the results
of applying a machine learning technique embedded in a standard ROOT package to
select IBD signals. To obtain a higher statistics, the signals and background
events in a gadolinium-loaded liquid scintillation detector were reproduced by
Monte Carlo simulation. We report the efficiencies of neutrino--induced $n-H$
and $n-Gd$ events selection using the machine learning technique.
