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Just to confirm if the following description falls in the category of ensemble learning. Suppose given a training set $D=\{(X,Y)\}$ we are asked to train a regressor. But now the way we do it is to train regressors on multiple hypothesis classes, say, one find the "closest" polynomial function of maximum degree 3, and another find the "closet" 2-layer neural network. And output the final result as, say, a weighted sum of these two. Can this also be called ensemble learning or there is some other terminologies used for it? The reason I feel confused is most of the ensemble learning algorithms I have seen assume one hypothesis space, like the final output is a weighted sum of decision tree, and what they do is just to simply adjust the weights?

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Yes, it is still an ensemble. With a simple weighted mean/sum in the end.

Wikipedia defines as:

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives

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