A survey of pattern recognition

This paper surveys the developments in the field of pattern classification to describe the current state-of-the-art. The author divides the pattern classification problem into deterministic and statistical approaches although, in many instances, both converge to the same result. The approaches are further subdivided into: Adalines and Madalines, linear discriminant functions, mathematical programming, mode-seeking, nearest neighbor, Bayes and minimax, statistical criteria, and fuzzy sets subclasses. A major effort is devoted to show relationships among procedures. The conditions under which procedures are equivalent are discussed. Such relationships are summarized through a graph. The problems of supervision, adaptive property, sample size, and sequential analysis are discussed.