Use of the industrial property system in Colombia (2018): A supervised learning application
Date
2020-06-22
2020-06-22
Author
Lis-Gutiérrez, Jenny-Paola
Lis Gutiérrez, Melissa
GALLEGO-TORRES, ADRIANA PATRICIA
Ballesteros Ballesteros, Vladimir
Romero-Ospina, Manuel-Francisco
Metadata
Show full item record
Show full item record
Abstract
The 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 trees
Collections