Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Konvolutionellt neuralt nätverk (klassificering)× | XGBoost× | |
|---|---|---|
| Ämnesområde≠ | Djupinlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 1998 | 2016 |
| Upphovsperson≠ | LeCun, Y. et al. | Chen, T. & Guestrin, C. |
| Typ≠ | Deep neural network (convolutional) | Ensemble (gradient-boosted decision trees) |
| Ursprungskälla≠ | LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier | XGBoost, extreme gradient boosting, scalable tree boosting |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced. | 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. |
| ScholarGateDatamängd ↗ |
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