Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Linear Online× | Aprendizado Online× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1960 (LMS); 1950 (RLS formalization) | 1958–2000s |
| Autor original≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Tipo≠ | Incremental supervised regression | Learning paradigm (sequential model update) |
| Fonte seminal≠ | Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Outros nomes | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | incremental learning, sequential learning, streaming learning, online machine learning |
| Relacionados | 6 | 6 |
| Resumo≠ | Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data-generating process can shift over time. | 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. |
| ScholarGateConjunto de dados ↗ |
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