Likelihood inference in nearest-neighbour classification models
Traditionally the neighbourhood size k in the k-nearest-neighbour algorithm is either fixed at the first nearest neighbour or is selected on the basis of a crossvalidation study. In this paper we present an alternative approach that develops the k-nearest-neighbour algorithm using likelihood-based i...
| Автори: | , |
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| Формат: | Journal article |
| Мова: | English |
| Опубліковано: |
2003
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