| 書目名稱 | Learning with Recurrent Neural Networks | | 編輯 | Barbara Hammer | | 視頻video | http://file.papertrans.cn/584/583032/583032.mp4 | | 概述 | The book details a new approach which enables neural networks to deal with symbolic data, folding networks.It presents both practical applications and a precise theoretical foundation | | 叢書名稱 | Lecture Notes in Control and Information Sciences | | 圖書封面 |  | | 描述 | 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 di | | 出版日期 | Book 2000 | | 關(guān)鍵詞 | Approximate capability; Folding networks; Learnability; artificial intelligence; artificial neural netwo | | 版次 | 1 | | doi | https://doi.org/10.1007/BFb0110016 | | isbn_softcover | 978-1-85233-343-0 | | isbn_ebook | 978-1-84628-567-7Series ISSN 0170-8643 Series E-ISSN 1610-7411 | | issn_series | 0170-8643 | | copyright | Springer-Verlag London 2000 |
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