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Neural Networks for Complex Data

Abstract : Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Université Paris 1
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https://hal.archives-ouvertes.fr/hal-00744929
Contributor : Fabrice Rossi <>
Submitted on : Wednesday, October 24, 2012 - 11:00:53 AM
Last modification on : Wednesday, August 26, 2020 - 11:52:04 AM
Long-term archiving on: : Saturday, December 17, 2016 - 4:07:00 AM

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Marie Cottrell, Madalina Olteanu, Fabrice Rossi, Joseph Rynkiewicz, Nathalie Villa-Vialaneix. Neural Networks for Complex Data. KI - Künstliche Intelligenz, Springer Nature, 2012, 26 (4), pp.373-380. ⟨10.1007/s13218-012-0207-2⟩. ⟨hal-00744929⟩

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