Visualizing output for a data learning algorithm

Daniel Carson; James Graham; Igor Ternovskiy

doi:10.1117/12.2228742

13 July 2016 Visualizing output for a data learning algorithm

Daniel Carson, James Graham, Igor Ternovskiy

Proceedings Volume 9826, Cyber Sensing 2016; 98260F (2016) https://doi.org/10.1117/12.2228742
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

This paper details the process we went through to visualize the output for our data learning algorithm. We have been developing a hierarchical self-structuring learning algorithm based around the general principles of the LaRue model. One example of a proposed application of this algorithm would be traffic analysis, chosen because it is conceptually easy to follow and there is a significant amount of already existing data and related research material with which to work with. While we choose the tracking of vehicles for our initial approach, it is by no means the only target of our algorithm. Flexibility is the end goal, however, we still need somewhere to start. To that end, this paper details our creation of the visualization GUI for our algorithm, the features we included and the initial results we obtained from our algorithm running a few of the traffic based scenarios we designed.

Citation Download Citation

Daniel Carson, James Graham, and Igor Ternovskiy "Visualizing output for a data learning algorithm", Proc. SPIE 9826, Cyber Sensing 2016, 98260F (13 July 2016); https://doi.org/10.1117/12.2228742

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