방법 비교

	다층 퍼셉트론 (MLP)	로지스틱 회귀	순환 신경망
분야	머신러닝	연구 통계	딥러닝
계열	Machine learning	Process / pipeline	Machine learning
기원 연도	1986	1958	1986–1990
창시자	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	David Roxbee Cox	Rumelhart, D. E.; Elman, J. L.
유형	Feedforward neural network (supervised learning)	Method	Sequential neural network
원전	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	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	logit model, binomial logistic regression, LR	RNN, Elman network, Jordan network, simple recurrent network
관련	4	3	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.