方法对比
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| 在线支持向量机× | 在线学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2005–2011 | 1958–2000s |
| 提出者≠ | Shalev-Shwartz, Singer, et al. (Pegasos); Bordes, Bottou et al. (LASVM) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| 类型≠ | Online kernel classifier | Learning paradigm (sequential model update) |
| 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 ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| 别名 | Online SVM, Incremental SVM, LASVM, Pegasos SVM | incremental learning, sequential learning, streaming learning, online machine learning |
| 相关≠ | 3 | 6 |
| 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 learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
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