Treelet Kernel Incorporating Chiral Information

Pierre-Anthony Grenier 1 Luc Brun 1 Didier Villemin 2
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Molecules being often described using a graph representation, graph kernels provide an interesting framework which allows to combine machine learning and graph theory in order to predict molecule's properties. However, some of these properties are induced both by relationships between the atoms of a molecule and by constraints on the relative positioning of these atoms. Graph kernels based solely on the graph representation of a molecule do not encode this relative positioning of atoms and are consequently unable to predict accurately some molecule's properties. This paper presents a new method which incorporates spatial constraints into the graph kernel framework in order to overcome this limitation.
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Communication dans un congrès
9th IAPR-TC15 International Workshop on Graph-based Representations in Pattern Recognition, May 2013, Vienne, Austria. pp.132-141, 2013
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https://hal.archives-ouvertes.fr/hal-00824172
Contributeur : Pierre-Anthony Grenier <>
Soumis le : mardi 21 mai 2013 - 11:36:59
Dernière modification le : jeudi 7 février 2019 - 17:31:00
Document(s) archivé(s) le : jeudi 22 août 2013 - 10:55:44

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  • HAL Id : hal-00824172, version 1

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Pierre-Anthony Grenier, Luc Brun, Didier Villemin. Treelet Kernel Incorporating Chiral Information. 9th IAPR-TC15 International Workshop on Graph-based Representations in Pattern Recognition, May 2013, Vienne, Austria. pp.132-141, 2013. 〈hal-00824172〉

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