An evolutionary approach to constraint-regularized learning.
Eyke Hüllermeier, Ingo Renners, Adolf Grauel (2004)
Mathware and Soft Computing
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The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to employ fuzzy set-based modeling techniques in order to express such knowledge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn ing process within the regularization framework...