Salīdzināt metodes

	Daudzslāņu perceptrons (MLP)	Logistiskā regresija	Atkārtotais neironu tīkls
Nozare	Dziļā mācīšanās	Pētniecības statistika	Dziļā mācīšanās
Saime	Machine learning	Process / pipeline	Machine learning
Izcelsmes gads	1986	1958	1986–1990
Autors	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	David Roxbee Cox	Rumelhart, D. E.; Elman, J. L.
Tips	Supervised feedforward neural network	Method	Sequential neural network
Pirmavots	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.
Citi nosaukumi	MLP, feedforward neural network, fully connected neural network, vanilla neural network	logit model, binomial logistic regression, LR	RNN, Elman network, Jordan network, simple recurrent network
Saistītās	4	3	3
Kopsavilkums	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of 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.