Classical and recent results in statistical pattern recognition and le
arning theory are reviewed in a two-class pattern classification setti
ng, This basic model best illustrates intuition and analysis technique
s while still containing the essential features and serving as a proto
type for many applications. Topics discussed include nearest neighbor,
kernel, and histogram methods, Vapnik-Chervonenkis theory, and neural
networks. The presentation and the large (though nonexhaustive) list
of references is geared to provide a useful overview of this field for
both specialists and nonspecialists.