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This chapter deals with the question of what it means for a set of classifiers to be optimal in the light of the available data, and how to provide a formal solution to this problem. As such, it tackles the core task of LCS, whose ultimate aim is it to find such a set.
Up until now there is no general definition of what LCS ought to learn. Rather, there is an intuitive understanding of what a desirable set of classifiers should look like, and LCS algorithms are designed around such an understanding. However, having LCS that perform according to intuition in simple problems where the desired solution is known does not mean that they will do so in more complex tasks. Furthermore, how do we know that our intuition does not betray us?
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© 2008 Springer-Verlag Berlin Heidelberg
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Drugowitsch, J. (2008). The Optimal Set of 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_7
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DOI: https://doi.org/10.1007/978-3-540-79866-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79865-1
Online ISBN: 978-3-540-79866-8
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