  Accurate photometric redshifts for large samples of galaxies are among the
main products of modern multiband digital surveys. Over the last decade, the
Sloan Digital Sky Survey (SDSS) has become a sort of benchmark against which to
test the various methods. We present an application of a new method to the
estimation of photometric redshifts for the galaxies in the SDSS Data Release 9
(SDSS-DR9). Photometric redshifts for more than 143 million galaxies were
produced and made available at the URL:
http://dame.dsf.unina.it/catalog/DR9PHOTOZ/. The MLPQNA (Multi Layer Perceptron
with Quasi Newton Algorithm) model provided within the framework of the
DAMEWARE (DAta Mining and Exploration Web Application REsource) is an
interpolative method derived from machine learning models. The obtained
redshifts have an overall uncertainty of sigma=0.023 with a very small average
bias of about 3x10^-5, and a fraction of catastrophic outliers of about 5%.
This result is slightly better than what was already available in the
literature, particularly in terms of the smaller fraction of catastrophic
outliers.
