Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Локальне лінійне вкладення (LLE)× | Метод опорних векторів (класифікація)× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2000 | 1995 |
| Автор методу≠ | Sam Roweis & Lawrence Saul | Cortes, C. & Vapnik, V. |
| Тип≠ | Nonlinear manifold dimensionality reduction | Maximum-margin classifier (kernel method) |
| Основоположне джерело≠ | Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Інші назви | LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Пов'язані≠ | 3 | 5 |
| Підсумок≠ | Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map. | 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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