Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Онлайн-метод опорних векторів× | Онлайнова логістична регресія× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2005–2011 | 1960s (perceptron); formalized for logistic loss ~2000s |
| Автор методу≠ | Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM) | Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L. |
| Тип≠ | Online kernel classifier | Incremental supervised classifier |
| Основоположне джерело≠ | Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30. DOI ↗ | Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag. link ↗ |
| Інші назви | Online SVM, Incremental SVM, LASVM, Pegasos SVM | incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier |
| Пов'язані≠ | 3 | 5 |
| Підсумок≠ | Online SVM adapts the classical support vector machine to streaming or sequentially arriving data by updating the decision boundary one example at a time rather than solving a global quadratic program. Algorithms such as Pegasos and LASVM make this tractable at large scale, preserving the margin-maximising spirit of SVMs with sub-linear time per update. | Online Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible. |
| ScholarGateНабір даних ↗ |
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