  We provide classifications for all 143 million non-repeat photometric objects
in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision
trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate
that these star/galaxy classifications are expected to be reliable for
approximately 22 million objects with r < ~20. The general machine learning
environment Data-to-Knowledge and supercomputing resources enabled extensive
investigation of the decision tree parameter space. This work presents the
first public release of objects classified in this way for an entire SDSS data
release. The objects are classified as either galaxy, star or nsng (neither
star nor galaxy), with an associated probability for each class. To demonstrate
how to effectively make use of these classifications, we perform several
important tests. First, we detail selection criteria within the probability
space defined by the three classes to extract samples of stars and galaxies to
a given completeness and efficiency. Second, we investigate the efficacy of the
classifications and the effect of extrapolating from the spectroscopic regime
by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF
QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic
training data, we effectively begin to extrapolate past our star-galaxy
training set at r ~ 18. By comparing the number counts of our training sample
with the classified sources, however, we find that our efficiencies appear to
remain robust to r ~ 20. As a result, we expect our classifications to be
accurate for 900,000 galaxies and 6.7 million stars, and remain robust via
extrapolation for a total of 8.0 million galaxies and 13.9 million stars.
[Abridged]
