Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences

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dc.contributor.author Sidorova, Julia
dc.contributor.author García Herrero, Jesús
dc.date.accessioned 2021-11-09T09:52:13Z
dc.date.available 2021-11-09T09:52:13Z
dc.date.issued 2015-11-01
dc.identifier.bibliographicCitation Sidorova, J., García, J. (2015). Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences. Pattern Recognition, 48, pp. 3749-3756. https://doi.org/10.1016/j.patcog.2015.05.001
dc.identifier.issn 0031-3203
dc.identifier.uri http://hdl.handle.net/10016/33552
dc.description.abstract To Integrate The Benefits Of Statistical Methods Into Syntactic Pattern Recognition, A Bridging Approach Is Proposed: (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. (C) 2015 Elsevier Ltd. All Rights Reserved.
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2015 Elsevier Ltd. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other syntactic pattern recognition
dc.subject.other grammatical inference
dc.subject.other feature segmentation
dc.subject.other smiles parser
dc.subject.other feature extraction
dc.title Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences
dc.type article
dc.subject.eciencia Informática
dc.identifier.doi https://doi.org/10.1016/j.patcog.2015.05.001
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 3749
dc.identifier.publicationissue 11
dc.identifier.publicationlastpage 3756
dc.identifier.publicationtitle PATTERN RECOGNITION
dc.identifier.publicationvolume 48
dc.identifier.uxxi AR/0000017158
dc.affiliation.dpto UC3M. Departamento de Informática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)
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