Using evolutionary computation to solve problems in nonparametric regression
Autor: | Chen Yu-ping, Pan Zheng-jun, Ding Lixin, Kang Li-shan |
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Rok vydání: | 1998 |
Předmět: |
Statistics::Theory
Weight function Mathematical optimization Multidisciplinary business.industry Nearest neighbour Machine learning computer.software_genre Evolutionary computation Nonparametric regression Statistics::Machine Learning ComputingMethodologies_PATTERNRECOGNITION Statistics::Methodology Artificial intelligence business computer Selection (genetic algorithm) Mathematics |
Zdroj: | Wuhan University Journal of Natural Sciences. 3:27-31 |
ISSN: | 1993-4998 1007-1202 |
DOI: | 10.1007/bf02827507 |
Popis: | This paper studies evolutionary mechanism of parameter selection in the construction of weight function for Nearest Neighbour Estimate in nonparametric regression. Construct an algorithm which adaptively evolves fine weight and makes good prediction about unknown points. The numerical experiments indicate that this method is effective. It is a meaningful discussion about practicability of nonparametric regression and methodology of adaptive model-building. |
Databáze: | OpenAIRE |
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