  We present techniques for the estimation of stellar atmospheric parameters
(Teff,logg,[Fe/H]) for stars from the SDSS/SEGUE survey. The atmospheric
parameters are derived from the observed medium-resolution (R=2000) stellar
spectra using non-linear regression models trained either on (1) pre-classified
observed data or (2) synthetic stellar spectra. In the first case we use our
models to automate and generalize parametrization produced by a preliminary
version of the SDSS/SEGUE Spectroscopic Parameter Pipeline (SSPP). In the
second case we directly model the mapping between synthetic spectra (derived
from Kurucz model atmospheres) and the atmospheric parameters, independently of
any intermediate estimates. After training, we apply our models to various
samples of SDSS spectra to derive atmospheric parameters, and compare our
results with those obtained previously by the SSPP for the same samples. We
obtain consistency between the two approaches, with RMS deviations of 150K in
Teff, 0.35dex in logg, and 0.22dex in [Fe/H]. The models are applied to
pre-processed spectra, either via Principal Components Analysis or a Wavelength
Range Selection method, which employs a subset of the full 3850-9000A spectral
range. This is both for computational reasons, and because it delivers higher
accuracy. From an analysis of cluster candidates with available SDSS
spectroscopy (M15, M13, M2, and NGC2420), we find evidence for small systematic
offsets in Teff and/or logg for the atmospheric parameter estimates from the
model trained on real data with the SSPP. Thus, this model turns out to derive
more precise, but less accurate, atmospheric parameters than the model trained
on synthetic data.
