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 language | English (US) |
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Pages (from-to) | 155-163 |
Number of pages | 9 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | PAMI-3 |
Issue number | 2 |
DOIs | |
State | Published - Mar 1981 |
Keywords
- 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