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...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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London
Springer London
2000
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| Schriftenreihe: | Lecture Notes in Control and Information Sciences
254 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHI01 Volltext |
| Zusammenfassung: | 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 theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation ability is investigated- including several new results for standard recurrent neural networks such as explicit bounds on the required number of neurons and the super Turing capability of sigmoidal recurrent networks. The information theoretical learnability is examined - including several contribution to distribution dependent learnability, an answer to an open question posed by Vidyasagar, and a generalisation of the recent luckiness framework to function classes. Finally, the complexity of training is considered - including new results on the loading problem for standard feedforward networks with an arbitrary multilayered architecture, a correlated number of neurons and training set size, a varying number of hidden neurons but fixed input dimension, or the sigmoidal activation function, respectively |
| Beschreibung: | 1 Online-Ressource (150 p) |
| ISBN: | 9781846285677 |
| DOI: | 10.1007/BFb0110016 |
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| 520 | |a 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 theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation ability is investigated- including several new results for standard recurrent neural networks such as explicit bounds on the required number of neurons and the super Turing capability of sigmoidal recurrent networks. The information theoretical learnability is examined - including several contribution to distribution dependent learnability, an answer to an open question posed by Vidyasagar, and a generalisation of the recent luckiness framework to function classes. Finally, the complexity of training is considered - including new results on the loading problem for standard feedforward networks with an arbitrary multilayered architecture, a correlated number of neurons and training set size, a varying number of hidden neurons but fixed input dimension, or the sigmoidal activation function, respectively | ||
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Datensatz im Suchindex
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| author | Hammer, Barbara 1970- |
| author_GND | (DE-588)13377435X |
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| dewey-full | 629.8 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
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| dewey-search | 629.8 |
| dewey-sort | 3629.8 |
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| discipline | Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
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| spelling | Hammer, Barbara 1970- Verfasser (DE-588)13377435X aut Learning with recurrent neural networks by Barbara Hammer London Springer London 2000 1 Online-Ressource (150 p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Control and Information Sciences 254 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 theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation ability is investigated- including several new results for standard recurrent neural networks such as explicit bounds on the required number of neurons and the super Turing capability of sigmoidal recurrent networks. The information theoretical learnability is examined - including several contribution to distribution dependent learnability, an answer to an open question posed by Vidyasagar, and a generalisation of the recent luckiness framework to function classes. Finally, the complexity of training is considered - including new results on the loading problem for standard feedforward networks with an arbitrary multilayered architecture, a correlated number of neurons and training set size, a varying number of hidden neurons but fixed input dimension, or the sigmoidal activation function, respectively Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Rekursives neuronales Netz (DE-588)4379549-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf NP-vollständiges Problem (DE-588)4138229-8 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Rekursives neuronales Netz (DE-588)4379549-3 s Maschinelles Lernen (DE-588)4193754-5 s NP-vollständiges Problem (DE-588)4138229-8 s 2\p DE-604 Neuronales Netz (DE-588)4226127-2 s 3\p DE-604 Erscheint auch als Druck-Ausgabe 9781852333430 https://doi.org/10.1007/BFb0110016 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hammer, Barbara 1970- Learning with recurrent neural networks Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Rekursives neuronales Netz (DE-588)4379549-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd NP-vollständiges Problem (DE-588)4138229-8 gnd |
| subject_GND | (DE-588)4379549-3 (DE-588)4193754-5 (DE-588)4226127-2 (DE-588)4138229-8 (DE-588)4113937-9 |
| title | Learning with recurrent neural networks |
| title_auth | Learning with recurrent neural networks |
| title_exact_search | Learning with recurrent neural networks |
| title_full | Learning with recurrent neural networks by Barbara Hammer |
| title_fullStr | Learning with recurrent neural networks by Barbara Hammer |
| title_full_unstemmed | Learning with recurrent neural networks by Barbara Hammer |
| title_short | Learning with recurrent neural networks |
| title_sort | learning with recurrent neural networks |
| topic | Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Rekursives neuronales Netz (DE-588)4379549-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd NP-vollständiges Problem (DE-588)4138229-8 gnd |
| topic_facet | Engineering Control, Robotics, Mechatronics Control engineering Robotics Mechatronics Rekursives neuronales Netz Maschinelles Lernen Neuronales Netz NP-vollständiges Problem Hochschulschrift |
| url | https://doi.org/10.1007/BFb0110016 |
| work_keys_str_mv | AT hammerbarbara learningwithrecurrentneuralnetworks |