An evolutionary approach to constraint-regularized learning.

Hüllermeier, Eyke, Renners, Ingo, and Grauel, Adolf. "An evolutionary approach to constraint-regularized learning.." Mathware and Soft Computing 11.2-3 (2004): 109-124. <http://eudml.org/doc/39266>.

@article{Hüllermeier2004,
abstract = {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.},
author = {Hüllermeier, Eyke, Renners, Ingo, Grauel, Adolf},
journal = {Mathware and Soft Computing},
keywords = {Inteligencia artificial; Algoritmos de aprendizaje; Lógica difusa; Algoritmos evolutivos},
language = {eng},
number = {2-3},
pages = {109-124},
title = {An evolutionary approach to constraint-regularized learning.},
url = {http://eudml.org/doc/39266},
volume = {11},
year = {2004},
}

TY - JOUR
AU - Hüllermeier, Eyke
AU - Renners, Ingo
AU - Grauel, Adolf
TI - An evolutionary approach to constraint-regularized learning.
JO - Mathware and Soft Computing
PY - 2004
VL - 11
IS - 2-3
SP - 109
EP - 124
AB - 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.
LA - eng
KW - Inteligencia artificial; Algoritmos de aprendizaje; Lógica difusa; Algoritmos evolutivos
UR - http://eudml.org/doc/39266
ER -