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[edit] Summary

A dependency graph for an artificial neural network. The variable h, a 3-dimensional vector, depends on x, the input variable. Then g, a 2-dimensional vector variable, depends on h, and finally, f, the output variable, depends on g. In this network, the vector variables can be further decomposed in parallel units. This means that h_1, for example, is independent of h_2 given x.

Christos Dimitrakakis 2005, created with xfig.

[edit] Licensing

I, the author of this work, hereby publish it under the following licenses:
GNU head Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License".

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