Machine learningMachine learning
在线高斯过程
在线高斯过程（OGP）将贝叶斯非参数GP框架扩展到流式或顺序到达的数据。OGP不为每个到达的观测值从头重新计算完整的GP后验，而是维护一个紧凑的摘要——稀疏的诱导点集——并对其进行增量更新，从而在实时和大规模场景下实现概率回归和分类。
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来源
- Csató, L. & Opper, M. (2002). Sparse on-line Gaussian processes. Neural Computation, 14(3), 641–668. DOI: 10.1162/089976602317250933 ↗
- Engel, Y., Mannor, S. & Meir, R. (2004). The kernel recursive least-squares algorithm. IEEE Transactions on Signal Processing, 52(8), 2275–2285. DOI: 10.1109/TSP.2004.830985 ↗
如何引用本页
ScholarGate. (2026, June 3). Online Gaussian Process Regression and Classification. ScholarGate. https://scholargate.app/zh/machine-learning/online-gaussian-process
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