手法を比較

	多層パーセプトロン (MLP)	リカレントニューラルネットワーク (RNN)	XGBoost
分野	機械学習	深層学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	1986	1986–1990	2016
提唱者	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	Rumelhart, D. E.; Elman, J. L.	Chen, T. & Guestrin, C.
種類	Feedforward neural network (supervised learning)	Sequential neural network	Ensemble (gradient-boosted decision trees)
原典	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
別名	MLP, feedforward neural network, fully connected neural network, artificial neural network	RNN, Elman network, Jordan network, simple recurrent network	XGBoost, extreme gradient boosting, scalable tree boosting
関連	4	3	5
概要	The Multi-layer Perceptron (MLP) is a feedforward neural network architecture trained by backpropagation, formalised by Rumelhart, Hinton, and Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons with nonlinear activation functions, and an output layer, the MLP can approximate any continuous function to arbitrary accuracy and serves as the conceptual bridge between classical machine learning and modern deep learning.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.