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A General Feed-forward Algorithm for Gradient Descent in Connectionist Networks

Sebastian ThrunFrank J. ŚmiejaGesellschaft für Mathematik und Datenverarbeitung

出版	Gesellschaft für Mathematik und Datenverarbeitung mbH, 1990
URL	http://books.google.com.hk/books?id=Z5EYmgEACAAJ&hl=&source=gbs_api

註釋Abstract: "An extended feed-forward algorithm for recurrent connectionist networks is presented. This algorithm, which works locally in time, is derived both for discrete-in-time networks and for continuous networks. Several standard gradient descent algorithms for connectionist networks (e.g. [48], [30], [28], [15], [34]), especially the backpropagation algorithm [36], are mathematically derived as a special case of this general algorithm. The learning algorithm presented in this paper is a superset of gradient descent learning algorithms for multilayer networks, recurrent networks and time-delay networks that allows any combinations of their components.