Analysis of multichannel internet communication. Page: 3 of 28
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Printed October 2004
Analysis of Multichannel Internet
Eric R. Robinson and Lisa A. Torrey
University of Wisconsin-Madison
Madison, WI 53706
Jeremy L. Newland and Andrew K. Theuninck
University of Minnesota-Duluth
Duluth, MN 55812
Richard F. Maclin and Charles E. Nove
Systems Technology Department
Sandia National Laboratories
P.O. Box 5800
Albuquerque, NM 87185-1397
A novel method employing machine-based learning to identify messages related to
other messages is described and evaluated. This technique may enable an analyst to
identify and correlate a small number of related messages from a large sample of
individual messages. The classic machine learning techniques of decision trees and
naive Bayes classification are seeded with few (or no) messages of interest and "learn" to
identify other related messages. The performance of this approach and these specific
learning techniques are evaluated and generalized.
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Nove, Charles E.; Maclin, Richard Frank; Theuninck, Andrew K. (University of Minnesota-Duluth, Duluth, MN); Newland, Jeremy L. (University of Minnesota-Duluth, Duluth, MN); Torrey, Lisa A. (University of Wisconsin-Madison, Madison, WI) & Robinson, Eric R. (University of Wisconsin-Madison, Madison, WI). Analysis of multichannel internet communication., report, October 1, 2004; United States. (digital.library.unt.edu/ark:/67531/metadc893889/m1/3/: accessed July 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.