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Cross Entropy for learning in Multi-Modal Streams

In this paper we present a variation of the Cross Entropy method that can be applied on Dynamic Bayesian Networks for efficient learning of the model parameters. We demonstrate the results achieved on real world video streams using a variety of DBNs. Finally we compare this approach to the traditional EM algorithm, in terms of computational complexity, memory requirements and robustness to initialization.