Titlebook: Computational Architectures Integrating Neural and Symbolic Processes; A Perspective on the Ron Sun,Lawrence A. Bookman Book 1995 Springer

持久

N. V. Banichuk,Pekka NeittaanmäkiI and connectionism. Some researchers .. have argued that symbolism and connectionism represent differing computational paradigms, while others have discussed merging the differing strengths and weaknesses of these approaches, as evidenced by the papers in this volume. The general consensus to date

思乡病

高谈阔论

N. V. Banichuk,Pekka Neittaanmäkiative retrieval of memories. The summation and thresholding of activation allows for smooth integration of multiple sources of knowledge. CNs with distributed representations (.) exhibit robustness in the face of noise/damage and can learn to perform complex mapping tasks just from examples. Connect

1FAWN

Relinquish

Hierarchical Architectures for ReasoningThis chapter has a threefold purpose: (1) to introduce a general framework for parallel/distributed computation, the .; (2) to expose in detail a symbolic example of a computational network, related to expert systems, called an .; and (3) to describe and investigate how an expert network can be realized as a neural network possessing a ..

吹牛者

Subsymbolic Parsing of Embedded Structures systems have been quite successful, for example, in modeling in-depth natural language processing [., ., .], episodic memory [., ., and problem solving [., ., .]. In such systems, knowledge is encoded in terms of explicit symbolic structures, and processing is based on handcrafted rules that operate on these structures.

啤酒

https://doi.org/10.1007/b102608Processing; artificial intelligence; cognitive psychology; cognitive science; computer; computer science;

finite

978-1-4757-8319-3Springer Science+Business Media New York 1995

paleolithic

Omnipotent