Definition
Ensemble learning is a machine learning paradigm where multiple learners are trained to solve the same problem. In contrast to ordinary machine learning approaches which try to learn one hypothesis from training data, ensemble methods try to construct a set of hypotheses and combine them to use.
Introduction
An ensemble contains a number of learners which are usually called base learners. The generalization ability of an ensemble is usually much stronger than that of base learners. Actually, ensemble learning is appealing because that it is able to boost weak learners which are slightly better than random guess to strong learnerswhich can make very accurate predictions. So, “base learners” are also referred as “weak learners”. It is noteworthy, however, that although most theoretical analyses work on weak learners, base learners used in practice are not necessarily weak since using...
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Zhou, ZH. (2009). Ensemble Learning. In: Li, S.Z., Jain, A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_293
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DOI: https://doi.org/10.1007/978-0-387-73003-5_293
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