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| Quadratische Diskriminanzanalyse (QDA)× | Naive Bayes× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie≠ | Latent structure | Machine learning |
| Entstehungsjahr≠ | 1939 | 1997 |
| Urheber≠ | Classical Gaussian discriminant analysis (Fisher / Welch lineage) | Mitchell, T. M. (textbook treatment) |
| Typ≠ | Generative Gaussian classifier | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Wegweisende Quelle≠ | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. ISBN: 978-0-387-84857-0 | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Aliasnamen≠ | QDA, quadratic classifier, kuadratik diskriminant analizi | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Verwandt≠ | 2 | 4 |
| Zusammenfassung≠ | Quadratic discriminant analysis is a generative classifier that models each class with its own multivariate Gaussian distribution, allowing each class a separate covariance matrix. Unlike linear discriminant analysis, which assumes a shared covariance and yields linear boundaries, QDA's per-class covariances produce curved (quadratic) decision boundaries, letting it capture differences in the spread and orientation of the classes. | 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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