Decision-making processes in pattern recognition (ACM monograph series)
Decision-making processes in pattern recognition (ACM monograph series)
IEEE Transactions on Computers
IEEE Computer Society Membership & Publications
IEEE Transactions on Computers
Design of a Random-Pulse Computer for Classifying Binary Patterns
IEEE Transactions on Computers
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
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This paper discusses a class of methods for pattern classification using a set of samples. They may also be used in reconstructing a probability density from samples. The methods discussed are potential function methods of a type directly derived from concepts related to superposition. The characteristics required of a potential function are examined, and it is shown that smooth potential functions exist that will separate arbitrary sets of sample points. Ideas suggested by Specht in regard to polynomial potential functions are extended.