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| 데مبر스터-셰퍼 증거 이론× | 나이브 베이즈× | |
|---|---|---|
| 분야≠ | 소프트 컴퓨팅 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1976 | 1997 |
| 창시자≠ | Arthur P. Dempster & Glenn Shafer | Mitchell, T. M. (textbook treatment) |
| 유형≠ | Uncertainty calculus for combining evidence | Probabilistic classifier (Bayes' theorem with conditional independence) |
| 원전≠ | Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| 별칭≠ | evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| 관련 | 4 | 4 |
| 요약≠ | Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion. | 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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