Learning with recurrent neural networks
Folding networks, a generalisation of recurrent neural networks to tree structured inputs, are investigated as a mechanism to learn regularities on classical symbolic data, for example. The architecture, the training mechanism, and several applications in different areas are explained. Afterwards a...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | Livre numérique |
| اللغة: | Anglais |
| منشور في: |
London ; Paris [etc.] :
Springer-Verlag London Limited : Springer e-books
[20..].
Cham : Springer Nature |
| سلاسل: | Lecture notes in control and information sciences
254 |
| الموضوعات: | |
| الوصول للمادة أونلاين: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| ملاحظة: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Learning with recurrent neural networks, Barbara Hammer, London, Springer, 2000, X-148 p., Lecture notes in control and information sciences, 1-85233-343-X • Learning with Recurrent Neural Networks, Texte imprimé, 9781447139591 |
جدول المحتويات:
- Introduction, Recurrent and Folding Networks: Definitions, Training, Background, Applications
- Approximation Ability: Foundationa, Approximation in Probability, Approximation in the Maximum Norm, Discussions and Open Questions
- Learnability: The Learning Scenario, PAC Learnability, Bounds on the VC-dimension of Folding Networks, Consquences for Learnability, Lower Bounds for the LRAAM, Discussion and Open Questions
- Complexity: The Loading Problem, The Perceptron Case, The Sigmoidal Case, Discussion and Open Questions
- Conclusion.