Abstract
It is shown that for n-valued conditionally independent features a large family of classifiers can be expressed as an (n—list-degree polynomial discriminant function. The usefulness of the polynomial expansion is discussed and demonstrated by considering the first-order Minkowski metric, the Euclidean distance, and Bayes’ classifiers for the ternary-feature case. Finally, some interesting side observations on the classifiers are made with respect to optimality and computational requirements.
Original language | English (US) |
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Pages (from-to) | 205-208 |
Number of pages | 4 |
Journal | IEEE Transactions on Computers |
Volume | C-21 |
Issue number | 2 |
DOIs | |
State | Published - Feb 1972 |
Keywords
- Bayes’
- classifier Euclidean distance classifier Minkowski metric classifier polynomial discriminant functions
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computational Theory and Mathematics