방법 비교

	확장된 CNN	양방향 RNN	랜덤 포레스트
분야	딥러닝	딥러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	2016	1997	2001
창시자	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Schuster, M. & Paliwal, K.K.	Breiman, L.
유형	Deep learning (dilated 1D convolutional network)	Recurrent neural network (sequence model)	Ensemble (bagging of decision trees)
원전	van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
별칭	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
관련	5	5	4
요약	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.