方法对比
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| 在线朴素贝叶斯× | 朴素贝叶斯 (Naive Bayes) 是一种快速的概率分类器，它应用贝叶斯定理，同时假设特征在给定类别时是条件独立的× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000s | 1997 |
| 提出者≠ | Adapted from traditional Naive Bayes; incremental form established by the data-stream mining community (Domingos, Hulten, and others, circa 2000) | Mitchell, T. M. (textbook treatment) |
| 类型≠ | Probabilistic classifier (online/incremental) | Probabilistic classifier (Bayes' theorem with conditional independence) |
| 开创性文献≠ | 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. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| 别名≠ | Incremental Naive Bayes, Streaming Naive Bayes, Naive Bayes with partial_fit, Online NB | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. | 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. |
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