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| K-Najbliższych Sąsiadów× | Naiwny Klasyfikator Bayesowski× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1967 | 1997 |
| Twórca≠ | Cover, T.M. & Hart, P.E. | Mitchell, T. M. (textbook treatment) |
| Typ≠ | Instance-based (non-parametric) learning | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Źródło pierwotne≠ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Inne nazwy≠ | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. |
| ScholarGateZbiór danych ↗ |
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