Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Stokastinen gradienttimenetelmä (SGD)× | XGBoost× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1951 | 2016 |
| Kehittäjä≠ | Robbins, H. & Monro, S. | Chen, T. & Guestrin, C. |
| Tyyppi≠ | First-order iterative optimization algorithm | Ensemble (gradient-boosted decision trees) |
| Alkuperäislähde≠ | Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Rinnakkaisnimet≠ | SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent | XGBoost, extreme gradient boosting, scalable tree boosting |
| Liittyvät≠ | 3 | 5 |
| Tiivistelmä≠ | Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateAineisto ↗ |
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