  The increasing importance of digital sky surveys collecting many millions of
galaxy images has reinforced the need for robust methods that can perform
morphological analysis of large galaxy image databases. Citizen science
initiatives such as Galaxy Zoo showed that large datasets of galaxy images can
be analyzed effectively by non-scientist volunteers, but since databases
generated by robotic telescopes grow much faster than the processing power of
any group of citizen scientists, it is clear that computer analysis is
required. Here we propose to use citizen science data for training machine
learning systems, and show experimental results demonstrating that machine
learning systems can be trained with citizen science data. Our findings show
that the performance of machine learning depends on the quality of the data,
which can be improved by using samples that have a high degree of agreement
between the citizen scientists. The source code of the method is publicly
available.
