Sangam: A Confluence of Knowledge Streams

An analysis of learning algorithms in complex stochastic environments

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dc.contributor Darken, Christian
dc.contributor Norbraten, Terry
dc.contributor Naval Postgraduate School
dc.creator Poor, Kristopher D.
dc.date 2012-03-14T17:38:19Z
dc.date 2012-03-14T17:38:19Z
dc.date 2007-06
dc.date.accessioned 2022-05-19T07:42:52Z
dc.date.available 2022-05-19T07:42:52Z
dc.identifier http://hdl.handle.net/10945/3413
dc.identifier 162129173
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/100152
dc.description As the military continues to expand its use of intelligent agents in a variety of operational aspects, event prediction and learning algorithms are becoming more and more important. In this paper, we conduct a detailed analysis of two such algorithms: Variable Order Markov and Look-Up Table models. Each model employs different parameters for prediction, and this study attempts to determine which model is more accurate in its prediction and why. We find the models contrast in that the Variable Order Markov Model increases its average prediction probability, our primary performance measure, with increased maximum model order, while the Look-Up Table Model decreases average prediction probability with increased recency time threshold. In addition, statistical tests of results of each model indicate a consistency in each model's prediction capabilities, and most of the variation in the results could be explained by model parameters.
dc.description http://archive.org/details/annalysisoflearn109453413
dc.description US Navy (USN) author.
dc.description Approved for public release; distribution is unlimited.
dc.format xiv, 49 p. ;
dc.format application/pdf
dc.publisher Monterey, California. Naval Postgraduate School
dc.subject Algorithms
dc.subject Prediction theory
dc.subject Markov processes
dc.title An analysis of learning algorithms in complex stochastic environments
dc.type Thesis


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