title: Recursive implementation of a two-step nonparametric decision rule.
abstract: The two-step approach to nonparametric discrimination is that of estimating class-conditional densities and deriving the Bayes decision rule as if the estimates were true. Direct implementation of such a decision rule ecounters two computational problems. Complexity increases with sample size, and finite precision limits the decision rule domain. Here a recursive algorithm to reduce the expected number of operations and word-length limitations below that of the direct approach is developed. A special case of the formulation reduces to the weighted k-nearest-neighbor rule.
authors.affiliation: Computer Science Section, Wayne State University, Detroit, MI 48202; Department of Computer Science, State University of New York at Buffalo, Amherst, NY 14226.
authors.forename: S N
authors.initials: SN
authors.lastname: Srihari