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An essential part of the introduced model and of LCS in general that hardly any research has been devoted to is how to combine the local models provided by the classifiers to produce a global model. More precisely, given an input and the output prediction of all matched classifiers, the task is to combine these predictions to form a global prediction. This task will be called the mixing problem, and some model that provides an approach to this task a mixing model.
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© 2008 Springer-Verlag Berlin Heidelberg
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Drugowitsch, J. (2008). Mixing Independently Trained Classifiers. In: Design and Analysis of Learning Classifier Systems. Studies in Computational Intelligence, vol 139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79866-8_6
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DOI: https://doi.org/10.1007/978-3-540-79866-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79865-1
Online ISBN: 978-3-540-79866-8
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