Similarity networks for classification: a case study in the Horse Colic problem

This paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input-weight similarities. The resulting...

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Detalles Bibliográficos
Autores: Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964, Hernández González, Jerónimo
Tipo de recurso: informe técnico
Fecha de publicación:2014
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/99450
Acceso en línea:https://hdl.handle.net/2117/99450
Access Level:acceso abierto
Palabra clave:Similarity measures
Neural networks
Horse Colic problem
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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spelling Similarity networks for classification: a case study in the Horse Colic problemBelanche Muñoz, Luis Antonio|||0000-0002-7577-1964Hernández González, JerónimoSimilarity measuresNeural networksHorse Colic problemÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialThis paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input-weight similarities. The resulting neuron model is capable of dealing directly with variables of potentially different nature (continuous, fuzzy, ordinal, categorical). There is also provision for missing values. The network is trained using a two-stage procedure very similar to that used to train a radial basis function (RBF) neural network. The network is compared to two types of RBF networks in a non-trivial dataset: the Horse Colic problem, taken as a case study and analyzed in detail.20142014-01-0120172017-01-17reporthttp://purl.org/coar/resource_type/c_93fcVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/2117/99450reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/994502026-05-27T15:37:01Z
dc.title.none.fl_str_mv Similarity networks for classification: a case study in the Horse Colic problem
title Similarity networks for classification: a case study in the Horse Colic problem
spellingShingle Similarity networks for classification: a case study in the Horse Colic problem
Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964
Similarity measures
Neural networks
Horse Colic problem
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Similarity networks for classification: a case study in the Horse Colic problem
title_full Similarity networks for classification: a case study in the Horse Colic problem
title_fullStr Similarity networks for classification: a case study in the Horse Colic problem
title_full_unstemmed Similarity networks for classification: a case study in the Horse Colic problem
title_sort Similarity networks for classification: a case study in the Horse Colic problem
dc.creator.none.fl_str_mv Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964
Hernández González, Jerónimo
author Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964
author_facet Belanche Muñoz, Luis Antonio|||0000-0002-7577-1964
Hernández González, Jerónimo
author_role author
author2 Hernández González, Jerónimo
author2_role author
dc.subject.none.fl_str_mv Similarity measures
Neural networks
Horse Colic problem
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Similarity measures
Neural networks
Horse Colic problem
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description This paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the mean of the partial input-weight similarities. The resulting neuron model is capable of dealing directly with variables of potentially different nature (continuous, fuzzy, ordinal, categorical). There is also provision for missing values. The network is trained using a two-stage procedure very similar to that used to train a radial basis function (RBF) neural network. The network is compared to two types of RBF networks in a non-trivial dataset: the Horse Colic problem, taken as a case study and analyzed in detail.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-01-01
2017
2017-01-17
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/99450
url https://hdl.handle.net/2117/99450
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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