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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.
In machine learning, feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret, [1] shorter training times, [2] to avoid the curse of dimensionality, [3]
Undominated pairs are represented as '221 vs (versus) 212' or, in terms of the total score equations, as 'a2 + b2 + c1 vs a2 + b1 + c2', etc. [Recall, as explained earlier, an 'undominated pair' is a pair of alternatives where one is characterized by a higher ranked category for at least one criterion and a lower ranked category for at least ...
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.
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.
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 ...
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis, this may be the selection of a statistical model from a set of candidate models, given data.
[citation needed] This shows that the ability to improve learning through distributed practice is not wholly dependent on either the hippocampus or the rhinal cortices but is dependent on the interaction between working memory abilities and the ability to form long-term memories, whether semantic or episodic, conscious or subconscious. [23]