قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| شبكة الالتفاف المتمددة (Dilated CNN)× | شبكة عصبية متكررة ثنائية الاتجاه× | الغابات العشوائية× | |
|---|---|---|---|
| المجال≠ | التعلم العميق | التعلم العميق | تعلم الآلة |
| العائلة | Machine learning | Machine learning | Machine learning |
| سنة النشأة≠ | 2016 | 1997 | 2001 |
| صاحب الطريقة≠ | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Schuster, M. & Paliwal, K.K. | Breiman, L. |
| النوع≠ | Deep learning (dilated 1D convolutional network) | Recurrent neural network (sequence model) | Ensemble (bagging of decision trees) |
| المصدر التأسيسي≠ | van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv. link ↗ | Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| الأسماء البديلة≠ | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| ذات صلة≠ | 5 | 5 | 4 |
| الملخص≠ | 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. | A Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surrounding context. With LSTM or GRU cells (BiLSTM/BiGRU) it is the standard approach for named-entity recognition, sequence labelling, and speech recognition. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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