
Learning with the Online EM Algorithm (Olivier Cappé)
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.
Información
- Programa
- Publicado16 de mayo de 2013, 10:00 p.m. UTC
- Duración1 h y 7 min
- ClasificaciónApto