STATISTICAL PATTERN CLASSIFICATION WITH BINARY VARIABLES.

Tzay Y. Young, Philip S. Liu, Romulo J. Rondon

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

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
Pages (from-to)155-163
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
VolumePAMI-3
Issue number2
StatePublished - Mar 1 1981

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Minimax Estimation
Discriminant Function
Binary Variables
Pattern Classification
Hamming Distance
Random Vector
Characteristic Function
Asymptotic Properties
Pattern recognition
Vector space
Modulo
Probability Distribution
Random variable
Entropy
Binary
Hamming distance
Formulation
Experimental Results
Vector spaces
Random variables

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

STATISTICAL PATTERN CLASSIFICATION WITH BINARY VARIABLES. / Young, Tzay Y.; Liu, Philip S.; Rondon, Romulo J.

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-3, No. 2, 01.03.1981, p. 155-163.

Research output: Contribution to journalArticle

Young, Tzay Y. ; Liu, Philip S. ; Rondon, Romulo J. / STATISTICAL PATTERN CLASSIFICATION WITH BINARY VARIABLES. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 1981 ; Vol. PAMI-3, No. 2. pp. 155-163.
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