Machine Learning for Computer Graphics: A Manifesto and Tutorial

Canmore, Canada October 08-October 10

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http://doi.ieeecomputersociety.org/10.1109/PCCGA.2003.1238242

11th Pacific Conference on Computer G ...

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	ASCII Text	x
	Aaron Hertzmann, "Machine Learning for Computer Graphics: A Manifesto and Tutorial," Computer Graphics and Applications, Pacific Conference on, pp. 22, 11th Pacific Conference on Computer Graphics and Applications (PG'03), 2003.

	BibTex	x
	@article{ 10.1109/PCCGA.2003.1238242, author = {Aaron Hertzmann}, title = {Machine Learning for Computer Graphics: A Manifesto and Tutorial}, journal ={Computer Graphics and Applications, Pacific Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-2028-6}, pages = {22}, doi = {http://doi.ieeecomputersociety.org/10.1109/PCCGA.2003.1238242}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer Graphics and Applications, Pacific Conference on TI - Machine Learning for Computer Graphics: A Manifesto and Tutorial SN - 0-7695-2028-6 SP EP A1 - Aaron Hertzmann, PY - 2003 KW - null VL - 0 JA - Computer Graphics and Applications, Pacific Conference on ER -

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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