Statistical Pattern Classification with Binary Variables

Tzay Y. Young, Philip S. Liu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Binary random variables are regarded as random vectors in a binary-field (modulo-2) linear vector space. A characteristic function is defined and related results derived using this formulation. Minimax estimation of probability distributions using an entropy criterion is investigated, which leads to an A-distribution and bilinear discriminant functions. Nonparametric classification approaches using Hamming distances and their asymptotic properties are discussed. Experimental results are presented.

Original languageEnglish (US)
Pages (from-to)155-163
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number2
StatePublished - Mar 1981


  • Binary data analysis
  • discriminant function
  • minimax estimation
  • pattern classification
  • statistical analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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