Learning with the Online EM Algorithm (Olivier Cappé)

from StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning" · host UP1 Service TICE

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.

Episode metadata supplied by the publisher feed · Published May 16, 2013

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Learning with the Online EM Algorithm (Olivier Cappé)

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