Polynomial Representation of Classifiers with Independent Discrete-Valued Features

Godfried Toussaint

    Research output: Contribution to journalArticle

    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 languageEnglish (US)
    Pages (from-to)205-208
    Number of pages4
    JournalIEEE Transactions on Computers
    VolumeC-21
    Issue number2
    DOIs
    StatePublished - Jan 1 1972

    Fingerprint

    Classifiers
    Classifier
    Polynomials
    Bayes Classifier
    Discriminant Function
    Polynomial
    Euclidean Distance
    Polynomial function
    Ternary
    Optimality
    First-order
    Metric
    Requirements
    Family
    Observation

    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

    Cite this

    Polynomial Representation of Classifiers with Independent Discrete-Valued Features. / Toussaint, Godfried.

    In: IEEE Transactions on Computers, Vol. C-21, No. 2, 01.01.1972, p. 205-208.

    Research output: Contribution to journalArticle

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