手法を比較

	Bidirectional RNN	ゲート付き再帰ユニット (GRU)	ランダムフォレスト
分野	深層学習	深層学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1997	2014	2001
提唱者	Schuster, M. & Paliwal, K.K.	Cho, K. et al.	Breiman, L.
種類	Recurrent neural network (sequence model)	Gated recurrent neural network unit	Ensemble (bagging of decision trees)
原典	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
別名	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
関連	5	5	4
概要	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.	The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.	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.