  Using an established classification technique, we leverage standard
observations and analyses to predict the progenitors of gamma-ray bursts
(GRBs). This technique, utilizing support vector machine (SVM) statistics,
provides a more nuanced prediction than the previous two-component Gaussian
mixture in duration of the prompt gamma-ray emission. Based on further
covariance testing from \textit{Fermi}-GBM, \textit{Swift}-BAT, and
\textit{Swift}-XRT data, we find that our classification based only on prompt
emission properties gives perspective on the recent evidence that mergers and
collapsars exist in both long and short GRB populations.
