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Multi-objective Racing Algorithm

Model selection is an important aspect of Machine Learning (ML). Aside from its traditional meaning, occasionally a “model” could also refer to any algorithm or learning strategy. Given an ensemble of candidate models, each model’s performance is assessed on a common set of test instances and, eventually, based on their performance on the aforementioned test, a single champion model or a subset of the ensemble is identified as the best for the given ML task.

Multi-Task Learning

Multi-Task Learning (MTL) is a machine learning paradigm, which is aimed to learn multiple related tasks simultaneously with information being shared across tasks. It is hoped that, with the help of the other tasks, the model of each task can be better trained, which leads to generalization performance. One practical example for MTL is the learning of multiple classification tasks simultaneously, each of which features a handwritten letter classification problems, such as "c" versus "e", "g" versus "y", etc. 

Metric Learning

Many Machine Learning algorithms entail the computation of distances, for example, k-nearest neighbor (KNN) for classification and k-Means for clustering. In most cases, when computing distances, the Euclidean distance metric is employed. However, the use of fixed distance metric may not necessarily perform well for all problems. Research Attention was directed to data-driven approaches in order to infer the best metric for a given problem.

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