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dc.creatorLis-Gutiérrez, Jenny-Paola
dc.creatorLis Gutiérrez, Melissa
dc.creatorBallesteros Ballesteros, Vladimir
dc.creatorRomero-Ospina, Manuel-Francisco
dc.description.abstractThe purpose of this paper is to establish ways to predict the spatial distribution of the use of the intellectual property system from information on industrial property applications and grants (distinctive signs and new creations) and copyright registrations in 2018. This will be done using supervised learning algorithms applied to information on industrial property applications and grants (trademarks and new creations) and copyright registrations in 2018. Within the findings, 4 algorithms were identified with a level of explanation higher than 80%: (i) Linear Regression, with an elastic network regularization; (ii) Stochastic Gradient Descent, with Hinge loss function, Ringe regularization (L2) and a constant learning rate; (iii) Neural Networks, with 1,000 layers, with Adam’s solution algorithm and 2,000 iterations; (iv) Random Forest, with 10 treesspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.sourceLecture Notes in Computer Sciencespa
dc.subjectSpatial distributionspa
dc.subjectDistinctive signsspa
dc.subjectNew creationsspa
dc.subjectSupervised learningspa
dc.subjectMachine learningspa
dc.titleUse of the industrial property system in Colombia (2018): A supervised learning applicationspa
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