Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Réseau de neurones convolutif (Classification)× | Machine à vecteurs de support (Classification)× | |
|---|---|---|
| Domaine≠ | Apprentissage profond | Apprentissage automatique |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1998 | 1995 |
| Auteur d'origine≠ | LeCun, Y. et al. | Cortes, C. & Vapnik, V. |
| Type≠ | Deep neural network (convolutional) | Maximum-margin classifier (kernel method) |
| Source fondatrice≠ | LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Alias | CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Apparentées | 5 | 5 |
| Résumé≠ | A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
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