Título inglés |
An evolutionary approach to constraint-regularized learning. |
Título español |
Un enfoque evolutivo en el aprendizaje regularizado con restricciones flexibles. |
Autor/es |
Hüllermeier, Eyke ; Renners, Ingo ; Grauel, Adolf |
Organización |
Informat. Inst. Marburg Univ., Marburg, Alemania;Center Comput. Intellig. Cogn. Syst., Soest, Alemania;Dep. Math. Univ. Paderborn-Soest, Soest, Alemania |
Revista |
1134-5632 |
Publicación |
2004, 11 (2-3): 109-124, 17 Ref. |
Tipo de documento |
articulo |
Idioma |
Inglés |
Resumen inglés |
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. |
Clasificación UNESCO |
120304 |
Palabras clave español |
Inteligencia artificial ; Algoritmos de aprendizaje ; Lógica difusa ; Algoritmos evolutivos |
Código MathReviews |
MR2139292 |
Código Z-Math |
Zbl 1129.68465 |
Acceso al artículo completo |