方法对比
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| 在线高斯混合模型× | 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≠ | 2000–2009 | 1958–2000s |
| 提出者≠ | Cappé, O. & Moulines, E. (online EM formulation) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| 类型≠ | Probabilistic clustering / density estimation (incremental) | Learning paradigm (sequential model update) |
| 开创性文献≠ | Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613. 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 GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM | incremental learning, sequential learning, streaming learning, online machine learning |
| 相关≠ | 5 | 6 |
| 摘要≠ | Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset. | 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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