Share Email Print

Proceedings Paper

Visualizing output for a data learning algorithm
Author(s): Daniel Carson; James Graham; Igor Ternovskiy
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 13 July 2016
PDF: 8 pages
Proc. SPIE 9826, Cyber Sensing 2016, 98260F (13 July 2016); doi: 10.1117/12.2228742
Show Author Affiliations
Daniel Carson, Riverside Research (United States)
Air Force Research Lab. (United States)
James Graham, 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)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?