方法证据记录
Online Gaussian Process
Online Gaussian Process (OGP) extends the Bayesian nonparametric GP framework to streaming or sequentially arriving data. Instead of recomputing the full GP posterior from scratch as each observation arrives, OGP maintains a compact summary — a sparse set of inducing points — and updates it incrementally, making probabilistic regression and classification feasible in real-time and large-scale settings.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Online Gaussian Process Regression and Classification
分类方法记录 · ml-model / machine-learning
- 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
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