  Although it is generally assumed that there are two dominant classes of
gamma-ray bursts (GRB) with different typical durations, it has been difficult
to unambiguously classify GRBs as short or long from summary properties such as
duration, spectral hardness, and spectral lag. Recent work used t-distributed
stochastic neighborhood embedding (t-SNE), a machine learning algorithm for
dimensionality reduction, to classify all Swift gamma-ray bursts as short or
long. Here, the method is expanded, using two algorithms, t-SNE and UMAP, to
produce embeddings that are used to provide a classification for the 1911 BATSE
bursts, 1321 Swift bursts, and 2294 Fermi bursts for which both spectra and
metadata are available. Although the embeddings appear to produce a clear
separation of each catalog into short and long bursts, a resampling-based
approach is used to show that a small fraction of bursts cannot be robustly
classified. Further, 3 of the 304 bursts observed by both Swift and Fermi have
robust but conflicting classifications. A likely interpretation is that in
addition to the two predominant classes of GRBs, there are additional, uncommon
types of bursts which may require multi-wavelength observations in order to
separate from more typical short and long GRBs.
