  Using a machine learning (ML) method, we mine DB white dwarfs (DBWDs) from
the Sloan Digital Sky Survey (SDSS) Data Release (DR) 12 and DR14. The ML
method consists of two parts: feature extraction and classification. The least
absolute shrinkage and selection operator (LASSO) is used for the spectral
feature extraction by comparing high quality data of a positive sample group
with negative sample groups. In both the training and testing sets, the
positive sample group is composed of a selection of 300 known DBWDs, while the
negative sample groups are obtained from all types of SDSS spectra. In the
space of the LASSO detected features, a support vector machine is then employed
to build classifiers that are used to separate the DBWDs from the non DBWDs for
each individual type. Depending on the classifiers, the DBWD candidates are
selected from the entire SDSS dataset. After visual inspection, 2808 spectra
(2029 objects) are spectroscopically confirmed. By checking the samples with
the literature, there are 58 objects with 60 spectra that are newly identified,
including a newly discovered AM CVn. Finally, we measure their effective
temperatures (Teff), surface gravities (log g), and radial velocities, before
compiling them into a catalog.
