dc.creatorLis-Gutiérrez, Jenny-Paola
dc.creatorZerda Sarmiento, Alvaro
dc.creatoramelec, viloria
dc.date2020-01-17T19:31:07Z
dc.date2020-01-17T19:31:07Z
dc.date2019
dc.date.accessioned2023-10-03T19:10:07Z
dc.date.available2023-10-03T19:10:07Z
dc.identifierhttp://hdl.handle.net/11323/5859
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9168473
dc.descriptionThe purpose of this research is to answer the following guiding question: how can the behavior of museum networks in Colombia be predicted with respect to the protection of intellectual property (copyright, confidential information and use of patents, domain names, industrial designs, use of trademarks) and the interaction of different types of proximity (geographical, organizational, relational, cognitive, cultural and institutional), based on the use of supervised learning algorithms? Among the main findings are that the best learning algorithms to predict the behavior of networks, considering different target variables are the AdaBoost, the naive Bayes and CN2 rule inducer
dc.formatapplication/pdf
dc.languageeng
dc.publisherUniversidad de la Costa
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subjectProximity
dc.subjectIntellectual property
dc.subjectIntellectual property management
dc.subjectMuseum
dc.subjectMuseum networks
dc.subjectMachine learning
dc.titleIntellectual property in colombian museums: an application of machine learning
dc.typePre-Publicación
dc.typehttp://purl.org/coar/resource_type/c_816b
dc.typeText
dc.typeinfo:eu-repo/semantics/preprint
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ARTOTR
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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