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Proceedings Paper

Implementing a self-structuring data learning algorithm
Author(s): James Graham; Daniel Carson; Igor Ternovskiy
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Paper Abstract

In this paper, we elaborate on what we did to implement our self-structuring data learning algorithm. To recap, we are working to develop a data learning algorithm that will eventually be capable of goal driven pattern learning and extrapolation of more complex patterns from less complex ones. At this point we have developed a conceptual framework for the algorithm, but have yet to discuss our actual implementation and the consideration and shortcuts we needed to take to create said implementation. We will elaborate on our initial setup of the algorithm and the scenarios we used to test our early stage algorithm. While we want this to be a general algorithm, it is necessary to start with a simple scenario or two to provide a viable development and testing environment. To that end, our discussion will be geared toward what we include in our initial implementation and why, as well as what concerns we may have. In the future, we expect to be able to apply our algorithm to a more general approach, but to do so within a reasonable time, we needed to pick a place to start.

Paper Details

Date Published: 22 June 2016
PDF: 7 pages
Proc. SPIE 9826, Cyber Sensing 2016, 98260G (22 June 2016); doi: 10.1117/12.2228743
Show Author Affiliations
James Graham, Ohio Univ. (United States)
Riverside Research (United States)
Daniel Carson, Riverside Research (United States)
Igor Ternovskiy, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 9826:
Cyber Sensing 2016
Igor V. Ternovskiy; Peter Chin, Editor(s)

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