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Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition
Title
Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition
Publication Type
Journal Article
Year of Publication
2012
Authors
Alemi-Neissi A
,
Baldassi C
,
Braunstein A
,
Pagnani A
,
Zecchina R
,
Zoccolan D
Journal
BMC Neuroscience
Volume
13
Pagination
1-2
URL
http://dx.doi.org/10.1186/1471-2202-13-S1-P2
DOI
10.1186/1471-2202-13-S1-P2
biocomp
neuroscience
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