Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition

Detalles Bibliográficos
Autores principales: Alemi-Neissi, Alireza, Baldassi, Carlo, Braunstein, Alfredo, Pagnani, Andrea, Zecchina, Riccardo, Zoccolan, Davide
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Poster Presentation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403536/
http://dx.doi.org/10.1186/1471-2202-13-S1-P2
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author Alemi-Neissi, Alireza
Baldassi, Carlo
Braunstein, Alfredo
Pagnani, Andrea
Zecchina, Riccardo
Zoccolan, Davide
author_facet Alemi-Neissi, Alireza
Baldassi, Carlo
Braunstein, Alfredo
Pagnani, Andrea
Zecchina, Riccardo
Zoccolan, Davide
author_sort Alemi-Neissi, Alireza
collection PubMed
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spelling pubmed-34035362012-07-27 Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition Alemi-Neissi, Alireza Baldassi, Carlo Braunstein, Alfredo Pagnani, Andrea Zecchina, Riccardo Zoccolan, Davide BMC Neurosci Poster Presentation BioMed Central 2012-07-16 /pmc/articles/PMC3403536/ http://dx.doi.org/10.1186/1471-2202-13-S1-P2 Text en Copyright ©2012 Alemi-Neissi et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Poster Presentation
Alemi-Neissi, Alireza
Baldassi, Carlo
Braunstein, Alfredo
Pagnani, Andrea
Zecchina, Riccardo
Zoccolan, Davide
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
title_full Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition
title_fullStr Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition
title_full_unstemmed Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition
title_short Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition
title_sort information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition
topic Poster Presentation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403536/
http://dx.doi.org/10.1186/1471-2202-13-S1-P2
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