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11th Pacific Conference on Computer Graphics and Applications (PG'03)
Machine Learning for Computer Graphics: A Manifesto and Tutorial
Canmore, Canada
October 08-October 10
ISBN: 0-7695-2028-6
Aaron Hertzmann, University of Toronto
I argue that computer graphics can bene.t from a deeper use of machine learning techniques. I give an overview of what learning has to offer the graphics community, with an emphasis on Bayesian techniques. I also attempt to address some misconceptions about learning, and to give a very brief tutorial on Bayesian reasoning.
Citation:
Aaron Hertzmann, "Machine Learning for Computer Graphics: A Manifesto and Tutorial," pg, pp.22, 11th Pacific Conference on Computer Graphics and Applications (PG'03), 2003
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