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English"Supervised classification of data affected by noise or error, with unknown probability distribution, is a challenging task. To this extend, we propose the Fuzzy Regularized Eigenvalue Classifier, based on a recent technique to classify data in two or more classes. We compare the execution time and accuracy of the classifier with other de facto standard methods. With the adoption of a novel membership function, the classifier is capable to produce more accurate models that well compare with results obtained by other methods, and fuzzy weighting functions, on benchmark datasets."
| Titolo: | Fuzzy regularized generalized eigenvalue classifier with a novel membership function | |
| Autori: | ||
| Data di pubblicazione: | 2013 | |
| Rivista: | ||
| Abstract: | "Supervised classification of data affected by noise or error, with unknown probability distribution, is a challenging task. To this extend, we propose the Fuzzy Regularized Eigenvalue Classifier, based on a recent technique to classify data in two or more classes. We compare the execution time and accuracy of the classifier with other de facto standard methods. With the adoption of a novel membership function, the classifier is capable to produce more accurate models that well compare with results obtained by other methods, and fuzzy weighting functions, on benchmark datasets." | |
| Handle: | http://hdl.handle.net/11591/320318 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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