Títol
A spectral learning framework for graphical models
Ponent
Raphaël Bailly
Lloc
Omega-S208
Dia
05/06/2012
Horari
11:00h
Abstract
I will present an extended version of the spectral algorithm which can be applied to graphs. This algorithm can be used as a learning algorithm for graphical models - directed and undirected. It can be used in a density estimation task on a distribution of labelled graphs. This algorithm is proven to converge, and is not prone to local extrema.
Slides
slides
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