Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Dilatoitu CNN× | Random Forest× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2016 | 2001 |
| Kehittäjä≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Breiman, L. |
| Tyyppi≠ | Deep learning (dilated 1D convolutional network) | Ensemble (bagging of decision trees) |
| Alkuperäislähde≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Rinnakkaisnimet≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Liittyvät≠ | 5 | 4 |
| 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateAineisto ↗ |
|
|