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Recursive Implementation of a Two-Step Nonparametric Decision Rule

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1 Author(s)
Sargur N. Srihari ; Computer Science Section, Wayne State University, Detroit, MI 48202; Department of Computer Science, State University of New York at Buffalo, Amherst, NY 14226.

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-1 ,  Issue: 1 )