This article deals with a weighted-type combined classiΓΏcation rule where the combining is based on a data discretization and the "weights" are determined by exponential kernels. The smoothing parameter of the kernel is estimated by a data-splitting approach. Both the mechanics and the asymptotic va
A consistent combined classification rule
β Scribed by M. Mojirsheibani
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 224 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0167-7152
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β¦ Synopsis
In this article we propose a data-based method for constructing combined classifiers. The resulting classifiers, which are linear in nature, turn out to be consistentβ’ (~) 1997 Elsevier Science B.V.
π SIMILAR VOLUMES
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This paper presents a novel boosting algorithm for genetic learning of fuzzy classiΓΏcation rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one
In this paper an optimum procedure, based on the maximum-likelihood criterion, for classification into one of two populations has been studied when multiple observations are available on the same variable for each individual. The distribution of the classification statistic, which turns out to be a