Description: Learning With Recurrent Neural Networks, Paperback by Hammer, Barbara, ISBN 185233343X, ISBN-13 9781852333430, Like New Used, Free shipping in the US 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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Book Title: Learning With Recurrent Neural Networks
Number of Pages: 150 Pages
Language: English
Publication Name: Learning with Recurrent Neural Networks
Publisher: Springer London, The Limited
Subject: Neural Networks, Electrical
Publication Year: 2000
Type: Textbook
Item Weight: 18.7 Oz
Author: Barbara Hammer
Item Length: 9.3 in
Subject Area: Computers, Technology & Engineering
Item Width: 6.1 in
Series: Lecture Notes in Control and Information Sciences Ser.
Format: Trade Paperback
