An evolutionary approach to constraint-regularized learning
| Autor: | Hüllermeier, Eyke, Renners, Ingo, Grauel, Adolf |
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| Jazyk: | Catalan; Valencian |
| Rok vydání: | 2004 |
| Předmět: | |
| Zdroj: | Mathware & soft computing; 2004: Vol.: 11 Núm.: 2-3 UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | 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 em- ploy fuzzy set-based modeling techniques in order to express such knowl- edge 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. |
| Databáze: | OpenAIRE |
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