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Algorithm selection is an important task in different domains of knowledge. Meta-learning treats this task by adopting a supervised learning strategy. Training examples in meta-learning (called meta-examples) are generated from experiments performed with a pool of candidate algorithms in a number of problems, usually collected from data repositories or synthetically generated. A meta-learner is then...
Meta-Learning aims to automatically acquire knowledge relating features of learning problems to the performance of learning algorithms. Each training example in Meta-Learning (i.e. each meta-example) stores features of a learning problem plus the performance obtained by a set of algorithms when evaluated on the problem. Based on a set of meta-examples, a Meta-Learner will be used to predict algorithm...
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