NUMERICAL EVALUATION OF CYTOLOGIC DATA .4. DISCRIMINATION AND CLASSIFICATION

  • 1 January 1980
    • journal article
    • research article
    • Vol. 2  (1) , 19-24
Abstract
When observed data must be assigned to 1 or another category, classification rules are needed. Linear discriminant functions provide easily computed rules: weighting the discriminant function according to the variances in the data sets helps reduce classification errors. Classification on the basis of a probability density involves nonlinear decision boundaries. Simple numerical examples for bivariate feature vectors are worked out to demonstrate these approaches to classification.

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