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 of inductive inference. After a brief review of this idea, the paper offers an operationalization of constraint regularized learning. The corresponding framework is based on evolutionary methods for model optimization and employs fuzzy rule bases of the Takagi Sugeno type as flexible function approximators.
@article{urn:eudml:doc:39266,
title = {An evolutionary approach to constraint-regularized learning.},
journal = {Mathware and Soft Computing},
volume = {11},
year = {2004},
pages = {109-124},
zbl = {1129.68465},
mrnumber = {MR2139292},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39266}
}
Hüllermeier, Eyke; Renners, Ingo; Grauel, Adolf. An evolutionary approach to constraint-regularized learning.. Mathware and Soft Computing, Tome 11 (2004) pp. 109-124. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39266/