Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Dilatoitu CNN× | XGBoost× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi | 2016 | 2016 |
| Kehittäjä≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Chen, T. & Guestrin, C. |
| Tyyppi≠ | Deep learning (dilated 1D convolutional network) | Ensemble (gradient-boosted decision trees) |
| Alkuperäislähde≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Rinnakkaisnimet≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | XGBoost, extreme gradient boosting, scalable tree boosting |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | A Dilated CNN is a one-dimensional convolutional network whose receptive field grows exponentially with depth, letting it model long-range structure in time series and audio signals. WaveNet (van den Oord et al., 2016) and the Temporal Convolutional Network of Bai, Kolter and Koltun (2018) are the prominent members of this family. | 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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