方法对比
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| 在线支持向量机× | 在线逻辑回归× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2005–2011 | 1960s (perceptron); formalized for logistic loss ~2000s |
| 提出者≠ | Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM) | Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L. |
| 类型≠ | Online kernel classifier | Incremental supervised classifier |
| 开创性文献≠ | 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 ↗ |
| 别名 | Online SVM, Incremental SVM, LASVM, Pegasos SVM | incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier |
| 相关≠ | 3 | 5 |
| 摘要≠ | 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. |
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