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Cicle de conferències 2014-2015. Lecture. Scallable resource-efficient learning from structured sequences

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor.author Sidorova, Julia
dc.date.accessioned 2015-04-27T09:26:03Z
dc.date.available 2015-04-27T09:26:03Z
dc.date.created 2015-04-10
dc.date.issued 2015-04-10
dc.identifier.uri http://hdl.handle.net/10854/4017
dc.description.abstract To integrate the benefits of statistical methods into syntactic pattern recognition, a Bridging Approach is proposed. Its steps are: (i) acquisition of a grammar per recognition class; (ii) comparison of the obtained grammars in order to find substructures of interest represented as sequences of terminal and/or non-terminal symbols and filling the feature vector with their counts; (iii) hierarchical feature selection and hierarchical classification, deducing and accounting for the domain taxonomy. The bridging approach has the benefits of syntactic methods: preserves structural relations and gives insights into the problem. Yet, it does not imply distance calculations and, thus, saves a non- trivial task-dependent design step. Instead it relies on statistical classification from many features. Our experiments concern a difficult problem of chemical toxicity prediction. The code and the data set are open-source. ca_ES
dc.format application/pdf
dc.format.extent 1 p. ca_ES
dc.language.iso eng ca_ES
dc.rights Tots els drets reservats ca_ES
dc.subject.other Biotecnologia -- Congressos ca_ES
dc.title Cicle de conferències 2014-2015. Lecture. Scallable resource-efficient learning from structured sequences ca_ES
dc.type info:eu-repo/semantics/other ca_ES
dc.rights.accesRights info:eu-repo/semantics/openAccess ca_ES

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