Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Пояснюваний багатошаровий перцептрон× | Багатошаровий перцептрон (БШП)× | |
|---|---|---|
| Галузь | Глибоке навчання | Глибоке навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2010s–present | 1986 |
| Автор методу≠ | Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. |
| Тип≠ | Supervised feedforward neural network with interpretability layer | Supervised feedforward neural network |
| Основоположне джерело≠ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ | Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ |
| Інші назви≠ | XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP | MLP, feedforward neural network, fully connected neural network, vanilla neural network |
| Пов'язані | 4 | 4 |
| Підсумок≠ | An Explainable Multilayer Perceptron (XMLP) is a standard feedforward neural network trained with backpropagation, augmented with post-hoc interpretability techniques — such as SHAP values, LIME, or integrated gradients — that attribute each prediction to individual input features. The combination retains the MLP's approximation power while satisfying transparency requirements common in regulated or high-stakes domains. | 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. |
| ScholarGateНабір даних ↗ |
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