Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Лінійний дискримінантний аналіз (LDA× | Метод опорних векторів (класифікація)× | |
|---|---|---|
| Галузь≠ | Статистика | Машинне навчання |
| Родина≠ | Hypothesis test | Machine learning |
| Рік появи≠ | 1936 | 1995 |
| Автор методу≠ | Ronald A. Fisher | Cortes, C. & Vapnik, V. |
| Тип≠ | Parametric linear classifier / dimensionality reduction | Maximum-margin classifier (kernel method) |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Пов'язані≠ | 7 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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