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ICANN
2005
Springer

CrySSMEx, a Novel Rule Extractor for Recurrent Neural Networks: Overview and Case Study

13 years 9 months ago
CrySSMEx, a Novel Rule Extractor for Recurrent Neural Networks: Overview and Case Study
In this paper, it will be shown that it is feasible to extract finite state machines in a domain of, for rule extraction, previously unencountered complexity. The algorithm used is called the Crystallizing Substochastic Sequential Machine Extractor, or CrySSMEx. It extracts the machine from sequence data generated from the RNN in interaction with its domain. CrySSMEx is parameter free, deterministic and generates a sequence of increasingly deterministic extracted stochastic models until a fully deterministic machine is found.
Henrik Jacobsson, Tom Ziemke
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICANN
Authors Henrik Jacobsson, Tom Ziemke
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