Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Head-driven PCFGs with latent-head statistics
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Discovering the hidden structure of complex dynamic systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation--Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.