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111
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ADVCS
2008
83views more  ADVCS 2008»
15 years 2 months ago
Information Flows in Causal Networks
We introduce a notion of causal independence based on virtual intervention, which is a fundamental concept of the theory of causal networks. Causal independence allows for de ning ...
Nihat Ay, Daniel Polani
134
Voted
ICNSC
2007
IEEE
15 years 10 months ago
Associative Memory for Noisy and Structurally Deformed Two-Dimensional Images Using Neural Networks
—This paper studies the problem of understanding noisy and structurally deformed two-dimensional images by means of abstractly defined neural works. First, in the framework of sy...
Hiroshi Inaba, Tomoki Takahashi, Keylan Alimhan
IJCNN
2006
IEEE
15 years 10 months ago
Effective Training Methods for Function Localization Neural Networks
— Inspired by Hebb’s cell assembly theory about how the brain worked, we have developed a function localization neural network (FLNN). The main part of a FLNN is structurally t...
Takafumi Sasakawa, Jinglu Hu, Katsunori Isono, Kot...
130
Voted
AUSDM
2006
Springer
125views Data Mining» more  AUSDM 2006»
15 years 7 months ago
Investigating the Size and Value Effect in Determining Performance of Australian Listed Companies: A Neural Network Approach
This paper explores the size and value effect in influencing performance of individual companies using backpropagation neural networks. According to existing theory, companies wit...
Justin Luu, Paul J. Kennedy
FMCAD
2006
Springer
15 years 7 months ago
Synchronous Elastic Networks
We formally define--at the stream transformer level--a class of synchronous circuits that tolerate any variability in the latency of their environment. We study behavioral properti...
Sava Krstic, Jordi Cortadella, Michael Kishinevsky...