方法对比
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| 线性判别分析 (LDA× | 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≠ | 统计学 | 机器学习 |
| 方法族≠ | Hypothesis test | Machine learning |
| 起源年份≠ | 1936 | 1997 |
| 提出者≠ | Ronald A. Fisher | Mitchell, T. M. (textbook treatment) |
| 类型≠ | Parametric linear classifier / dimensionality reduction | Probabilistic classifier (Bayes' theorem with conditional independence) |
| 开创性文献≠ | Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| 别名 | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| 相关≠ | 7 | 4 |
| 摘要≠ | Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA. | 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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