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Contrato de aprendizaje No. 2 1996 -1997
(Servicio Nacional de Aprendizaje (SENA)División de promoción y mercadeo de servicios de la dirección General, 2017)
Vinculación
(Servicio Nacional de Aprendizaje (SENA)Subdirección General de Operaciones. División Programación Didáctica, 2018)
Conformity Assessment as a Tool for Organizational Learning in Large engineering and Construction Projects
(Universidad Alberto Hurtado. Facultad de Economía y Negocios, 2014)
A simulation of contract farming using agent based modeling
(FGV EAESP, 2016)
Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection
(2019-02-01)
Background and Objective: Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices ...
Learning-by-employing: the value of commitment under uncertainty
(Univ Chicago Press, 2016-07)
We analyze commitment to employment in an environment in which an infinitely lived firm faces a sequence of finitely lived workers who differ in their ability. A worker's ability is initially unknown, and a worker's effort ...
Machine Learning model for managing risk on Procurement Contracts
(Biblioteca Digital wdg.biblioUniversidad de Guadalajara, 2022-07-11)
In current time, the process of making decisions in companies through the data is an important task to be competitive Worldwide.
One of the most important areas inside companies is the Procurement department. This area ...
Blockchain-based federated learning for intelligent control in Heavy Haul Railway
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the ...
Managing load contract restrictions with online learning
(IEEE, 2017)
Demand Response (DR) is an effective means of providing flexibility in power systems facing increased variability from renewables. Aggregators must dispatch loads for demand response which provide the most useful services ...
Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?
(Elsevier, 2015)
We develop a general equilibrium asset pricing model under incomplete information and rational learning in order to understand the unexplained predictability of option prices. In our model, the fundamental dividend growth ...