Minimum cross-entropy approximation for modeling of highly intertwining data sets at subclass levels

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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.

Original languageEnglish (US)
Pages (from-to)139-152
Number of pages14
JournalJournal of Intelligent Information Systems
Volume11
Issue number2
DOIs
Publication statusPublished - Sep 1 1998

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Keywords

  • Cross-entropy
  • Cross-entropy minimization
  • Intertwining data sets
  • Probability distribution
  • Subclasses

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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