Ensemble learning

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Ensemble Learning

This note presents a chronological review of the literature on ensemble learning which has accumulated over the past twenty years. The idea of ensemble learning is to employ multiple learners and combine their predictions. If we have a committee of M models with uncorrelated errors, simply by averaging them the average error of a model can be reduced by a factor of M. Unfortunately, the key ass...

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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 learners which can make very accurate predictions. So, “base learners” are als...

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This chapter gives a tutorial introduction to Ensemble Learning, a recently developed Bayesian method. For many problems it is intractable to perform inferences using the true posterior density over the unknown variables. Ensemble Learning allows the true posterior to be approximated by a simpler approximate distribution for which the required inferences are tractable.

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Ensemble Learning refers to the procedures employed to train multiple learning machines and combine their outputs, treating them as a “committee” of decision makers. The principle is that the committee decision, with individual predictions combined appropriately, should have better overall accuracy, on average, than any individual committee member. Numerous empirical and theoretical studies hav...

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A new optimization technique is proposed for classifiers fusion — Cooperative Coevolutionary Ensemble Learning (CCEL). It is based on a specific multipopulational evolutionary algorithm — cooperative coevolution. It can be used as a wrapper over any kind of weak algorithms, learning procedures and fusion functions, for both classification and regression tasks. Experiments on the real-world prob...

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ژورنال

عنوان ژورنال: Scholarpedia

سال: 2009

ISSN: 1941-6016

DOI: 10.4249/scholarpedia.2776