Learning Multi-Class Dynamics

Blake, Andrew, North, Ben, Isard, Michael

Neural Information Processing Systems 

Yule-Walker) are available for learning Auto-Regressive process models of simple, directly observable, dynamical processes.When sensor noise means that dynamics are observed only approximately, learning can still been achieved via Expectation-Maximisation (EM) together with Kalman Filtering. However, this does not handle more complex dynamics, involving multiple classes of motion.

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