Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Регрессия по методу наименьших медиан квадратов (LMS)× | RANSAC-регрессия× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1984 | 1981 |
| Автор метода≠ | Peter J. Rousseeuw | Fischler & Bolles |
| Тип | Robust linear regression | Robust linear regression |
| Основополагающий источник≠ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ | Fischler, M. A. & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24(6), 381-395. DOI ↗ |
| Другие названия≠ | LMS, least median of squares regression, en küçük medyan kareler (LMS) | random sample consensus, RANSAC, robust regression, RANSAC Regresyonu |
| Связанные | 5 | 5 |
| Сводка≠ | Least Median of Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of minimising the sum of squared residuals like ordinary least squares, it minimises the median of the squared residuals, which lets the fit resist contamination by up to roughly 50% outliers. | RANSAC Regression is a robust linear regression method introduced by Fischler and Bolles in 1981 that fits a model to the inlier points of a dataset while automatically excluding outliers. Instead of fitting all the data at once, it repeatedly samples small subsets, fits a candidate model, and keeps the model that wins the largest consensus of agreeing points. |
| ScholarGateНабор данных ↗ |
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