Machine learningMachine learning
在线支持向量机
在线支持向量机（Online SVM）通过一次处理一个样本而非求解全局二次规划问题来更新决策边界，从而使经典支持向量机适应流式或顺序到达的数据。Pegasos和LASVM等算法使其在大规模数据上变得可行，在每次更新中以亚线性时间保持了支持向量机（SVM）的最大间隔精神。
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来源
- 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: 10.1007/s10107-010-0420-4 ↗
- Bordes, A., Ertekin, S., Weston, J., & Bottou, L. (2005). Fast kernel classifiers with online and active learning. Journal of Machine Learning Research, 6, 1579–1619. link ↗
如何引用本页
ScholarGate. (2026, June 3). Online Support Vector Machine (Incremental SVM for Streaming Data). ScholarGate. https://scholargate.app/zh/machine-learning/online-support-vector-machine
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