Topics in Statistical Pattern Recognition

The nearest-neighbor and potential function decision rules are nonparametric techniques that partition the feature space based on a set of labelled sample points. Determining whether the partitions of the two rules are identical for a given set of points is an interesting problem in computational geometry. Here, a relationship between the two methods in terms of subclasses and composite classes is developed. Considering an exponential potential function, necessary and sufficient conditions for identity of their decision surfaces are obtained. Based on conditions of symmetry, weiohting, and the Voronoi re qion of a point, an algorithm for establishing identity in IR d is introduced.

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The convergent method of constructing polynomial discriminant functions for pattern recognition

Flasiński

Lewicki

1991

Pattern Recognition

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Topics in Statistical Pattern Recognition

Cited by 6 publications

References 78 publications

Automatic Selection of a Discrimination Rule Based upon Minimization of the Empirical Risk

Automatic Selection of a Discrimination Rule Based upon Minimization of the Empirical Risk

An algorithm for determining identity of nearest-neighbor and potential function decision rules

The convergent method of constructing polynomial discriminant functions for pattern recognition

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