  Exploration of time domain is now a vibrant area of research in astronomy,
driven by the advent of digital synoptic sky surveys. While panoramic surveys
can detect variable or transient events, typically some follow-up observations
are needed; for short-lived phenomena, a rapid response is essential. Ability
to automatically classify and prioritize transient events for follow-up studies
becomes critical as the data rates increase. We have been developing such
methods using the data streams from the Palomar-Quest survey, the Catalina Sky
Survey and others, using the VOEventNet framework. The goal is to automatically
classify transient events, using the new measurements, combined with archival
data (previous and multi-wavelength measurements), and contextual information
(e.g., Galactic or ecliptic latitude, presence of a possible host galaxy
nearby, etc.); and to iterate them dynamically as the follow-up data come in
(e.g., light curves or colors). We have been investigating Bayesian
methodologies for classification, as well as discriminated follow-up to
optimize the use of available resources, including Naive Bayesian approach, and
the non-parametric Gaussian process regression. We will also be deploying
variants of the traditional machine learning techniques such as Neural Nets and
Support Vector Machines on datasets of reliably classified transients as they
build up.
