Optimal weighted nearest neighbour classifiers
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Summary
An asymptotic expansion for the excess risk (regret) of a weighted nearest-neighbour classifier is derived, and it is argued that improvements in the rate of convergence are possible under stronger smoothness assumptions, provided the authors allow negative weights.
- Type
- article
- Published
- 2011-01-30
- Cited by
- 291
- References
- 41
- Access
- Open access
- OpenAlex
- https://openalex.org/W1965524806
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:88511688
Keywords
Nearest neighbour, Classifier (UML), Regret, Pattern recognition (psychology), Asymptotically optimal algorithm
References
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- Exact bootstrap k-nearest neighbor learners
- Fast learning rates for plug-in classifiers
- Results in statistical discriminant analysis: a review of the former Soviet union literature
- Support vector machines
- Theory of classification : a survey of some recent advances
- Book Review: Tubes
- Optimal aggregation of classifiers in statistical learning
- Smooth Discrimination Analysis
- Consistent Nonparametric Regression
- Heuristics of instability and stabilization in model selection
- Bandwidth choice for nonparametric classification
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- An Information-Theoretic Approach for Setting the Optimal Number of Decision Trees in Random Forests
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- Decadal Trend in Agricultural Abandonment and Woodland Expansion in an Agro-Pastoral Transition Band in Northern China
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