Vertaile menetelmiä
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| Monikerki-kerrosperceptron (MLP)× | Logistinen regressio× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Tutkimuksen tilastomenetelmät |
| Menetelmäperhe≠ | Machine learning | Process / pipeline |
| Syntyvuosi≠ | 1986 | 1958 |
| Kehittäjä≠ | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. | David Roxbee Cox |
| Tyyppi≠ | Supervised feedforward neural network | Method |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet≠ | MLP, feedforward neural network, fully connected neural network, vanilla neural network | logit model, binomial logistic regression, LR |
| Liittyvät≠ | 4 | 3 |
| Tiivistelmä≠ | 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. |
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