Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Онлайн-лінійна регресія× | Онлайнова логістична регресія× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 1960 (LMS); 1950 (RLS formalization) | 1960s (perceptron); formalized for logistic loss ~2000s |
| Автор методу≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L. |
| Тип≠ | Incremental supervised regression | Incremental supervised classifier |
| Основоположне джерело≠ | Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag. link ↗ |
| Інші назви | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data-generating process can shift over time. | 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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