Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| RNN Bidirecional× | CNN Dilatada× | Random Forest× | |
|---|---|---|---|
| Área≠ | Aprendizado profundo | Aprendizado profundo | Aprendizado de máquina |
| Família | Machine learning | Machine learning | Machine learning |
| Ano de origem≠ | 1997 | 2016 | 2001 |
| Autor original≠ | Schuster, M. & Paliwal, K.K. | van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V. | Breiman, L. |
| Tipo≠ | Recurrent neural network (sequence model) | Deep learning (dilated 1D convolutional network) | Ensemble (bagging of decision trees) |
| Fonte seminal≠ | Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. DOI ↗ | 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 ↗ |
| Outros nomes≠ | Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU | Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 5 | 5 | 4 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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