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Article
Minimum Cross-Entropy Approximation for Modeling of Highly Intertwining Data Sets at Subclass Levels
Journal of Intelligent Information Systems 11
  • Qiuming Zhu, University of Nebraska at Omaha
Document Type
Article
Publication Date
9-1-1998
Disciplines
Abstract
We study the problem of how to accurately model the data sets that contain a number of highly intertwining sets in terms of their spatial distributions. Applying the Minimum Cross-Entropy minimization technique, the data sets are placed into a minimum number of subclass clusters according to their high intraclass and low interclass similarities. The method leads to a derivation of the probability density functions for the data sets at the subclass levels. These functions then, in combination, serve as an approximation to the underlying functions that describe the statistical features of each data set.
Comments

The final publication is available at Springer via http://link.springer.com/article/10.1023%2FA%3A1008680819565.

Citation Information
Qiuming Zhu. "Minimum Cross-Entropy Approximation for Modeling of Highly Intertwining Data Sets at Subclass Levels" Journal of Intelligent Information Systems 11 Vol. 11 Iss. 2 (1998) p. 139 - 152
Available at: http://works.bepress.com/qiuming-zhu/24/