Nearest Neighbor Pattern Classification.¶
Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21-27.
Cited by¶
2 citations across 2 artifacts.
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Primes¶
- Classification
- Decision trees, logistic regression, support-vector machines, and neural networks learn classifiers from labeled training data, building on foundational pattern-classification results such as Cover and Hart (1967) on nearest-neighbor decision rules.
This sourceFoundational result in machine-learning classification: establishes the asymptotic error bound of the nearest-neighbor decision rule, anchoring large-scale automated category assignment.
- Decision trees, logistic regression, support-vector machines, and neural networks learn classifiers from labeled training data, building on foundational pattern-classification results such as Cover and Hart (1967) on nearest-neighbor decision rules.
- Exemplar Retrieval
- In machine learning k-nearest-neighbours, kernel methods, and retrieval-augmented generation are exemplar systems, while logistic regression, decision trees, and parametric classifiers are prototype systems; vector-database retrieval feeding a model is the most visible recent return to exemplar architecture at scale.
This sourceFoundational analysis of the k-nearest-neighbour classifier, the canonical non-parametric, instance-based (exemplar) method whose model is the stored training set itself; the same case base under a different distance metric yields different answers.
- In machine learning k-nearest-neighbours, kernel methods, and retrieval-augmented generation are exemplar systems, while logistic regression, decision trees, and parametric classifiers are prototype systems; vector-database retrieval feeding a model is the most visible recent return to exemplar architecture at scale.
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