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An evolutionary approach to constraint-regularized learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Hüllermeier, Eyke Renners, Ingo Grauel, Adolf |
| Copyright Year | 2004 |
| 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. |
| Starting Page | 109 |
| Ending Page | 124 |
| Page Count | 16 |
| File Format | PDF HTM / HTML |
| Volume Number | 11 |
| Alternate Webpage(s) | http://upcommons.upc.edu/bitstream/handle/2099/3641/5-hullermeier.pdf;sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
