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Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately.
In this example a company should prefer product B's risk and payoffs under realistic risk preference coefficients. Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine).
Solving such problems is the focus of multiple-criteria decision analysis (MCDA). This area of decision-making, although long established, has attracted the interest of many researchers and practitioners and is still highly debated as there are many MCDA methods which may yield very different results when they are applied to exactly the same ...
The development of MCP models can be made either through direct or indirect approaches. Direct techniques involve the specification of all parameters of the decision model (e.g., the weights of the criteria) through an interactive procedure, where the decision analyst elicits the required information from the decision-maker.
Several studies show that majority of students can achieve mastery in a learning task, but the time that they need to spend on is different. [ 18 ] [ 19 ] Bloom argues that there are 1 to 5 percent of students who have special talent for learning a subject (especially music and foreign languages) and there are also around five percent of ...
Abstract-concept learning is seeing the comparison of the stimuli based on a rule (e.g., identity, difference, oddity, greater than, addition, subtraction) and when it is a novel stimulus. [9] With abstract-concept learning have three criteria’s to rule out any alternative explanations to define the novelty of the stimuli.
The purpose of standards-based assessment [5] is to connect evidence of learning to learning outcomes (the standards). When standards are explicit and clear, the learner becomes aware of their achievement with reference to the standards, and the teacher may use assessment data to give meaningful feedback to students about this progress.