  We describe the development of a system for an automated, iterative,
real-time classification of transient events discovered in synoptic sky
surveys. The system under development incorporates a number of Machine Learning
techniques, mostly using Bayesian approaches, due to the sparse nature,
heterogeneity, and variable incompleteness of the available data. The
classifications are improved iteratively as the new measurements are obtained.
One novel feature is the development of an automated follow-up recommendation
engine, that suggest those measurements that would be the most advantageous in
terms of resolving classification ambiguities and/or characterization of the
astrophysically most interesting objects, given a set of available follow-up
assets and their cost functions. This illustrates the symbiotic relationship of
astronomy and applied computer science through the emerging discipline of
AstroInformatics.
