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2019 | OriginalPaper | Chapter

Intellectual Property in Colombian Museums: An Application of Machine Learning

Authors : Jenny Paola Lis-Gutiérrez, Álvaro Zerda Sarmiento, Amelec Viloria

Published in: Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Publisher: Springer International Publishing

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Abstract

The 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.

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Footnotes
1
According to Resolution 1975 of 2013 of the Ministry of Culture, a thematic network is an “organizational form that links the different agents of the museum sector according to the themes of interest, common administrative forms and typologies of collection” (article 1).
 
2
In accordance with Resolution 1975 of 2013 of the Ministry of Culture, a territorial network is an “organizational form that links the different agents of the museum sector located in the different territories of the country” (article 1).
 
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Metadata
Title
Intellectual Property in Colombian Museums: An Application of Machine Learning
Authors
Jenny Paola Lis-Gutiérrez
Álvaro Zerda Sarmiento
Amelec Viloria
Copyright Year
2019
Book
Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Print ISBN: 978-3-030-36203-4

Electronic ISBN: 978-3-030-36204-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-36204-1_24

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