  The application of supervised artificial neural networks (ANNs) for quasar
selection from combined radio and optical surveys with photometric and
morphological data is investigated, using the list of candidates and their
classification from White et al. (2000). Seven input parameters and one output,
evaluated to 1 for quasars and 0 for nonquasars, were used, with architectures
7:1 and 7:2:1. Both models were trained on samples of ~800 sources and yielded
similar performance on independent test samples, with reliability as large as
87% at 80% completeness. For comparison the quasar fraction from the original
candidate list was 56%. The accuracy is similar to that found by White et al.
using supervised learning with oblique decision trees. This performance
probably approaches the maximum value achievable with this database.
Probabilities for the 98 candidates without spectroscopic classification in
White et al. are presented and compared with the results from their work,
showing a good agreement. This is the first analysis of the performance of ANNs
for the selection of quasars. Our work shows that ANNs provide a promising
technique for the selection of specific object types in astronomical databases.
