Distinguishing short and long $Fermi$ gamma-ray bursts
  Two classes of gamma-ray bursts (GRBs), short and long, have been determined
without any doubts, and are usually ascribed to different progenitors, yet
these classes overlap for a variety of descriptive parameters. A subsample of
46 long and 22 short $Fermi$ GRBs with estimated Hurst Exponents (HEs),
complemented by minimum variability time-scales (MVTS) and durations ($T_{90}$)
is used to perform a supervised Machine Learning (ML) and Monte Carlo (MC)
simulation using a Support Vector Machine (SVM) algorithm. It is found that
while $T_{90}$ itself performs very well in distinguishing short and long GRBs,
the overall success ratio is higher when the training set is complemented by
MVTS and HE. These results may allow to introduce a new (non-linear) parameter
that might provide less ambiguous classification of GRBs.
