
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.
Информация
- Подкаст
- Опубликовано16 мая 2013 г. в 22:00 UTC
- Длительность1 ч. 7 мин.
- ОграниченияБез ненормативной лексики