Abstract : Chemoinformatics is a well established research eld concerned with the discovery of molecule's properties through informational tech- niques. Computer science's research elds mainly concerned by chemoin- formatics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework combining machine learning and graph theory techniques. Such kernels prove their e - ciency on several chemoinformatics problems and this paper presents two new graph kernels applied to regression and classi cation prob- lems. The rst kernel is based on the notion of edit distance while the second is based on subtrees enumeration. The design of this last kernel is based on a variable selection step in order to obtain kernels de ned on parsimonious sets of patterns. Performances of both kernels are investigated through experiments.
https://hal.archives-ouvertes.fr/hal-00773283
Contributeur : Luc Brun
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Soumis le : samedi 12 janvier 2013 - 19:06:05
Dernière modification le : jeudi 7 février 2019 - 17:11:55
Document(s) archivé(s) le : samedi 13 avril 2013 - 04:08:11
