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| Multilayer-Perzeptron (MLP)× | Logistische Regression× | |
|---|---|---|
| Fachgebiet≠ | Maschinelles Lernen | Forschungsstatistik |
| Familie≠ | Machine learning | Process / pipeline |
| Entstehungsjahr≠ | 1986 | 1958 |
| Urheber≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. | David Roxbee Cox |
| Typ≠ | Feedforward neural network (supervised learning) | Method |
| Wegweisende Quelle≠ | Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Aliasnamen≠ | MLP, feedforward neural network, fully connected neural network, artificial neural network | logit model, binomial logistic regression, LR |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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. |
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