Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Lineární diskriminační analýza (LDA× | Stroj s podpůrnými vektory (klasifikace)× | |
|---|---|---|
| Obor≠ | Statistika | Strojové učení |
| Rodina≠ | Hypothesis test | Machine learning |
| Rok vzniku≠ | 1936 | 1995 |
| Tvůrce≠ | Ronald A. Fisher | Cortes, C. & Vapnik, V. |
| Typ≠ | Parametric linear classifier / dimensionality reduction | Maximum-margin classifier (kernel method) |
| Původní zdroj≠ | Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Další názvy≠ | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Příbuzné≠ | 7 | 5 |
| Shrnutí≠ | 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. | 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. |
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