  In the modern galaxy surveys photometric redshifts play a central role in a
broad range of studies, from gravitational lensing and dark matter distribution
to galaxy evolution. Using a dataset of about 25,000 galaxies from the second
data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts
with five different methods: (i) Random forest, (ii) Multi Layer Perceptron
with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization
network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian
Photometric Redshift model (or BPZ) and (v) a classical SED template fitting
procedure (Le Phare). We show how SED fitting techniques could provide useful
information on the galaxy spectral type which can be used to improve the
capability of machine learning methods constraining systematic errors and
reduce the occurrence of catastrophic outliers. We use such classification to
train specialized regression estimators, by demonstrating that such hybrid
approach, involving SED fitting and machine learning in a single collaborative
framework, is capable to improve the overall prediction accuracy of photometric
redshifts.
