首页|Findings from Miguel Hernandez University of Elche Broaden Understanding of Machine Learning (Evaluating Different Methods for Ranking Inputs In the Context of the Performance Assessment of Decision Making Units: a Machine Learning Approach)
Findings from Miguel Hernandez University of Elche Broaden Understanding of Machine Learning (Evaluating Different Methods for Ranking Inputs In the Context of the Performance Assessment of Decision Making Units: a Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Machine Learning. According to news reporting originating from Alicante, Spain, by NewsRx correspondents, research stated, "In the context of assessing the performance of decision-making units (companies, institutions, etc.), it is important to know the contribution or importance of each input to the generation of products and services in the production process. Identifying the degree of relevance of each input is a challenge from both an applied and a methodological point of view, especially within the field of non-parametric techniques, such as Data Envelopment Analysis (DEA), where the mathematical expression of the production function associated with the data generating process is not specified." Financial supporters for this research include Ministerio de Ciencia e Innovacion/Agencia Estatal de Investigacion, Center for Forestry Research & Experimentation (CIEF), Catedra Santander en Eficiencia y Productividad, Miguel Hernandez University (UMH), Valencian Community (Spain).
AlicanteSpainEuropeCyborgsEmerging TechnologiesMachine LearningMiguel Hernandez University of Elche
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