title: Numerical evaluation of cytologic data. IV. Discrimination and classification.
abstract: When observed data have to be assigned to one or another category, classification rules are needed. Linear discriminant functions provide easily computed rules; weighing the discriminat 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.
mesh_terms: D003584:Cytological Techniques; D008433:Mathematics; D011336:Probability; D013223:Statistics as Topic