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Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component analysis, ranking and classification of data using the Ward method, linear discriminant analysis, and Gaussian regression among others.
Clariandys Rivera-Kempis; Leobardo Valera; Miguel Sastre-Castillo. Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs. Sustainability 2021, 13, 8252 .
AMA StyleClariandys Rivera-Kempis, Leobardo Valera, Miguel Sastre-Castillo. Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs. Sustainability. 2021; 13 (15):8252.
Chicago/Turabian StyleClariandys Rivera-Kempis; Leobardo Valera; Miguel Sastre-Castillo. 2021. "Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs." Sustainability 13, no. 15: 8252.
The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, when the original dynamical system is very large, the resulting reduced-order model, although much “thinner”, is still as tall as the original system, i.e., it has the same number of equations. In some extreme cases, the number of equations is prohibitive and cannot be loaded in memory. In this work, we combine traditional interval constraint solving techniques with a strategy to reduce the number of equations to consider. We describe our approach and report preliminary promising results.
Omeiza Olumoye; Glen Throneberry; Angel Garcia; Leobardo Valera; Abdessattar Abdelkefi; Martine Ceberio. Solving Large Dynamical Systems by Constraint Sampling. Communications in Computer and Information Science 2019, 3 -15.
AMA StyleOmeiza Olumoye, Glen Throneberry, Angel Garcia, Leobardo Valera, Abdessattar Abdelkefi, Martine Ceberio. Solving Large Dynamical Systems by Constraint Sampling. Communications in Computer and Information Science. 2019; ():3-15.
Chicago/Turabian StyleOmeiza Olumoye; Glen Throneberry; Angel Garcia; Leobardo Valera; Abdessattar Abdelkefi; Martine Ceberio. 2019. "Solving Large Dynamical Systems by Constraint Sampling." Communications in Computer and Information Science , no. : 3-15.