Improved classification of genomic data by Gram-Schmidt feature selection

This work explains some important aspects in the world of the neural networks, as the classification methods and the procedures of feature selection. Moreover, there is a practical part that consists in creating a program that provides us the useful information to do the classification. It is import...

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Detalles Bibliográficos
Autor: Marco Reales, Jose Maria
Tipo de recurso: tesis de maestría
Fecha de publicación:2010
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:20.500.14342/2770
Acceso en línea:http://hdl.handle.net/20.500.14342/2770
Access Level:acceso abierto
Palabra clave:Xarxes neuronals (Informàtica) -- TFM
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spelling Improved classification of genomic data by Gram-Schmidt feature selectionMarco Reales, Jose MariaXarxes neuronals (Informàtica) -- TFM00462This work explains some important aspects in the world of the neural networks, as the classification methods and the procedures of feature selection. Moreover, there is a practical part that consists in creating a program that provides us the useful information to do the classification. It is important to consider that in this thesis we have touched some biochemical aspects because the program has been designed for bioinformatics applications. Therefore the first part of the work consists in an introduction to genomics, namely, relations of enzymes and amino-acids. Finally all the results obtained in the work have been reported and discussed.Universitat Ramon Llull. La Salle2010info:eu-repo/semantics/masterThesis108 p.http://hdl.handle.net/20.500.14342/2770reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésENG TFM MUEXT;1865Attribution-NonCommercial-NoDerivatives 4.0 International© Escola Tècnica Superior d'Enginyeria La Sallehttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:20.500.14342/27702026-05-29T05:05:01Z
dc.title.none.fl_str_mv Improved classification of genomic data by Gram-Schmidt feature selection
title Improved classification of genomic data by Gram-Schmidt feature selection
spellingShingle Improved classification of genomic data by Gram-Schmidt feature selection
Marco Reales, Jose Maria
Xarxes neuronals (Informàtica) -- TFM
004
62
title_short Improved classification of genomic data by Gram-Schmidt feature selection
title_full Improved classification of genomic data by Gram-Schmidt feature selection
title_fullStr Improved classification of genomic data by Gram-Schmidt feature selection
title_full_unstemmed Improved classification of genomic data by Gram-Schmidt feature selection
title_sort Improved classification of genomic data by Gram-Schmidt feature selection
dc.creator.none.fl_str_mv Marco Reales, Jose Maria
author Marco Reales, Jose Maria
author_facet Marco Reales, Jose Maria
author_role author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. La Salle
dc.subject.none.fl_str_mv Xarxes neuronals (Informàtica) -- TFM
004
62
topic Xarxes neuronals (Informàtica) -- TFM
004
62
description This work explains some important aspects in the world of the neural networks, as the classification methods and the procedures of feature selection. Moreover, there is a practical part that consists in creating a program that provides us the useful information to do the classification. It is important to consider that in this thesis we have touched some biochemical aspects because the program has been designed for bioinformatics applications. Therefore the first part of the work consists in an introduction to genomics, namely, relations of enzymes and amino-acids. Finally all the results obtained in the work have been reported and discussed.
publishDate 2010
dc.date.none.fl_str_mv 2010
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.14342/2770
url http://hdl.handle.net/20.500.14342/2770
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv ENG TFM MUEXT;1865
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
© Escola Tècnica Superior d'Enginyeria La Salle
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
© Escola Tècnica Superior d'Enginyeria La Salle
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 108 p.
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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