Learning multi-class dynamics
Andrew Blake, Ben North and Michael Isard.
Advances in Neural
Information Processing Systems 11, 389-395, MIT Press, (1999).
Abstract
Standard techniques (Yule-Walker) are available for learning
Auto-Regressive process models of dynamical processes. When sensor
noise means that dynamics are observed only approximately, learning
has still been achieved via Expectation-Maximisation (EM) together
with Kalman Filtering. This cannot handle more complex dynamics,
involving multiple classes of motion. For that case, we show here
how EM can be combined with the {\sc Condensation} algorithm, which
is based on propagation of random sample-sets. Experiments have been
performed with visually observed juggling, and plausible dynamical
models are found to emerge from the learning process.
Click here for a
compressed (gzip) postscript
version
If you want to read more details, especially on the experiments with
juggling, a
compressed (gzip) postscript
version of a full (draft) report is available.
Back to
Michael Isard's home page