Learning a class of large finite state machines with a recurrent neural network
β Scribed by C. Lee Giles; B.G. Horne; T. Lin
- Book ID
- 108019971
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 655 KB
- Volume
- 8
- Category
- Article
- ISSN
- 0893-6080
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
In this paper the efficiency of recurrent neural network implementations of m-state finite state machines will be explored. Specifically, it will be shown that the node complexity for the unrestricted case can be bounded above by O(v/-m-). It will also be shown that the node complexity is 0( ~) when
The ability of a neural network with one hidden layer to accurately learn a specified learning set increases with the number of nodes in the hidden layer; if a network has exactly the same number of internal nodes as the number of examples to be learnt, it is theoretically able to learn these exampl