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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
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
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@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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