방법 비교

	다층 퍼셉트론 (MLP)	랜덤 포레스트	순환 신경망
분야	머신러닝	머신러닝	딥러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1986	2001	1986–1990
창시자	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	Breiman, L.	Rumelhart, D. E.; Elman, J. L.
유형	Feedforward neural network (supervised learning)	Ensemble (bagging of decision trees)	Sequential neural network
원전	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
별칭	MLP, feedforward neural network, fully connected neural network, artificial neural network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	RNN, Elman network, Jordan network, simple recurrent network
관련	4	4	3
요약	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.	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.	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.