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TNN
1998
92views more  TNN 1998»
15 years 6 months ago
Inductive inference from noisy examples using the hybrid finite state filter
—Recurrent neural networks processing symbolic strings can be regarded as adaptive neural parsers. Given a set of positive and negative examples, picked up from a given language,...
Marco Gori, Marco Maggini, Enrico Martinelli, Giov...
NN
1998
Springer
108views Neural Networks» more  NN 1998»
15 years 6 months ago
How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies
Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NA...
Tsungnan Lin, Bill G. Horne, C. Lee Giles
IJCNN
2006
IEEE
16 years 12 days ago
Knowledge Representation and Possible Worlds for Neural Networks
— The semantics of neural networks can be analyzed mathematically as a distributed system of knowledge and as systems of possible worlds expressed in the knowledge. Learning in a...
Michael J. Healy, Thomas P. Caudell
NCA
1998
IEEE
15 years 6 months ago
A Neural Network Model of a Communication Network with Information Servers
This paper models information flow in a communication network. The network consists of nodes that communicate with each other, and information servers that have a predominantly o...
Philippe De Wilde
GECCO
2006
Springer
132views Optimization» more  GECCO 2006»
15 years 10 months ago
A neural evolutionary approach to financial modeling
This paper presents an approach to the joint optimization of neural network structure and weights which can take advantage of backpropagation as a specialized decoder. The approac...
Antonia Azzini, Andrea Tettamanzi