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| Наивен Бейс× | Дърво на решенията× | Методът на опорните вектори (класификация)× | |
|---|---|---|---|
| Област | Машинно обучение | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Година на възникване≠ | 1997 | 1984 | 1995 |
| Създател≠ | Mitchell, T. M. (textbook treatment) | Breiman, Friedman, Olshen & Stone | Cortes, C. & Vapnik, V. |
| Тип≠ | Probabilistic classifier (Bayes' theorem with conditional independence) | Recursive partitioning (if-then rules) | Maximum-margin classifier (kernel method) |
| Основополагащ източник≠ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Други названия≠ | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Свързани≠ | 4 | 5 | 5 |
| Резюме≠ | 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. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
| ScholarGateНабор от данни ↗ |
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