626 search results - page 33 / 126 » Evolving Artificial Neural Networks that Develop in Time |
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JMLR
2010
14 years 7 months ago
2010
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
89
Voted
EUROGP
2001
Springer
15 years 5 months ago
2001
Springer
This paper describes how animat-based “food foraging” techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented the fo...
134
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IJCNN
2008
IEEE
15 years 7 months ago
2008
IEEE
Abstract— This paper proposes a combination of methodologies based on a recent development –called Extreme Learning Machine (ELM)– decreasing drastically the training time of...
IWANN
2009
Springer
15 years 7 months ago
2009
Springer
This paper presents an algorithm, different from the classical time series, specialised in extracting knowledge from time series. The algorithm,
84
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IWINAC
2007
Springer
15 years 6 months ago
2007
Springer
We consider a notion of morphism of neural networks and develop its properties. We show how, given any definite logic program P, the least fixed point of the immediate consequenc...