  In recent years, automated, supervised classification techniques have been
fruitfully applied to labeling and organizing large astronomical databases.
These methods require off-line classifier training, based on labeled examples
from each of the (known) object classes. In practice, only a small batch of
labeled examples, hand-labeled by a human expert, may be available for
training. Moreover, there may be no labeled examples for some classes present
in the data, i.e. the database may contain several unknown classes. Unknown
classes may be present due to 1) uncertainty in or lack of knowledge of the
measurement process, 2) an inability to adequately ``survey'' a massive
database to assess its content (classes), and/or 3) an incomplete scientific
hypothesis. In recent work, new class discovery in mixed labeled/unlabeled data
was formally posed, with a proposed solution based on mixture models. In this
work we investigate this approach, propose a competing technique suitable for
class discovery in neural networks, and evaluate both methods for
classification and class discovery on several astronomical data sets. Our
results demonstrate up to a 57% reduction in classification error compared to a
standard neural network classifier that uses only labeled data.
