Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Regresioni Linear Online× | Regresioni linear (ML)× | |
|---|---|---|
| Fusha | Mësimi i makinës | Mësimi i makinës |
| Familja | Machine learning | Machine learning |
| Viti i origjinës≠ | 1960 (LMS); 1950 (RLS formalization) | 1805–1809 |
| Krijuesi≠ | Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS) | Legendre, A.-M. & Gauss, C.F. |
| Lloji≠ | Incremental supervised regression | Supervised regression |
| Burimi themelues≠ | Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ | Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7 |
| Emërtime të tjera | incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression | ordinary least squares regression, OLS, least squares regression, multiple linear regression |
| Të lidhura≠ | 6 | 5 |
| Përmbledhja≠ | 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. | Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task. |
| ScholarGateSeti i të dhënave ↗ |
|
|