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Learning with the Online EM Algorithm (Olivier Cappé)

16 mai 2013
The Online Expectation-Maximization (EM) is a generic algorithm that can be used to
estimate the parameters of latent data models incrementally from large volumes of data. The general principle of the approach is to use a stochastic approximation scheme, in the domain of sufficient statistics, as a proxy for a limiting, deterministic, population version of the EM recursion. In this talk, I will briefly review the convergence properties of the method and discuss some applications and extensions of the basic approach.

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