Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Atbalsta vektoru mašīna (klasifikācija)× | Naive Bayes× | Random Forest× | |
|---|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1995 | 1997 | 2001 |
| Autors≠ | Cortes, C. & Vapnik, V. | Mitchell, T. M. (textbook treatment) | Breiman, L. |
| Tips≠ | Maximum-margin classifier (kernel method) | Probabilistic classifier (Bayes' theorem with conditional independence) | Ensemble (bagging of decision trees) |
| Pirmavots≠ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Citi nosaukumi≠ | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Saistītās≠ | 5 | 4 | 4 |
| Kopsavilkums≠ | 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. | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
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