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 the work of White et al. (2000, Cat. J/ApJS/126/133>) Seven input parameters and one output, evaluated to 1 for quasars and 0 for non-quasars during the training, 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 per cent at 80 per cent completeness (or 90 to 80 per cent for completeness from 70 to 90 per cent). For comparison, the quasar fraction from the original candidate list was 56 per cent.

Cone search capability for table J/MNRAS/353/211/table4 (Quasar probabilities for the 98 candidates without spectroscopic classification in White et al. (2000, Cat. ))