Abstract
Assume that X is the input variable defined on the domain ℝk, and Y is the output variable defined on the domain ℝ. Now assume that we have a training data set DB = {(x 1 j ,…, x k j ,y j ) : j = 1,…, N}. We now consider how to derive a linguistic rule base from this training data set, which can fit this training data set accurately and at the same time has a high generalization capability. In the following we firstly propose a rule induction method which is very simple and natural. Then in order to improve the generalization capability of the rule base we present a clustering based method to coarsen the rule base.
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© 2014 Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg
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Qin, Z., Tang, Y. (2014). Prototype Theory for Learning. In: Uncertainty Modeling for Data Mining. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41251-6_10
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DOI: https://doi.org/10.1007/978-3-642-41251-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41250-9
Online ISBN: 978-3-642-41251-6
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