  There are many occasions when one does not have complete information in order
to classify objects into different classes, and yet it is important to do the
best one can since other decisions depend on that. In astronomy, especially
time-domain astronomy, this situation is common when a transient is detected
and one wishes to determine what it is in order to decide if one must follow
it. We propose to use the Difference Boosting Neural Network (DBNN) which can
boost differences between feature vectors of different objects in order to
differentiate between them. We apply it to the publicly available data of the
Catalina Real-Time Transient Survey (CRTS) and present preliminary results. We
also describe another use with a stellar spectral library to identify spectra
based on a few features. The technique itself is more general and can be
applied to a varied class of problems.
