Comparer des méthodes

	K-plus-proches voisins en ligne	Naive Bayes en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2010s (formalized in streaming-learning literature)	2000s
Auteur d'origine	Extension of Fix & Hodges (1951) KNN to the streaming/online setting; notable online variant by Losing et al. (2016)	Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000)
Type	Instance-based online classifier/regressor	Probabilistic classifier (online/incremental)
Source fondatrice	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.	Domingos, P. & Hulten, G. (2000). Mining high-speed data streams. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71–80. ACM.
Alias	Online KNN, Incremental KNN, Streaming KNN, KNN with concept drift adaptation	Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB
Apparentées	5	6
Résumé	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.	Online Naive Bayes is an incremental adaptation of the classical Naive Bayes classifier that updates its class-conditional statistics one observation (or one mini-batch) at a time, making it well suited to data streams, very large datasets that cannot be held in memory, and settings where the model must adapt continuously as new labeled examples arrive.