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Online K-nearest neighbors

Online K-Nearest Neighbors (Online KNN) adapts the classic KNN algorithm to a data-stream setting where observations arrive sequentially and the model must update incrementally without full retraining. Instead of storing all historical instances, it maintains a bounded sliding window or adaptive memory, using the most recent and most representative examples to classify or predict each incoming point by proximity.

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Online K-Nearest Neighbors (Incremental KNN for Data Streams)

Registro del método taxonómico · ml-model / machine-learning

Losing, V., Hammer, B., & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300. IEEE. · DOI 10.1109/ICDM.2016.0040
Gama, J. (2010). Knowledge Discovery from Data Streams. CRC Press / Chapman & Hall. · ISBN 978-1-4398-2611-9

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Métodos relacionados

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