Using Multiple Statistical Prototypes to Classify Continuously Valued Data
نویسندگان
چکیده
Multiple Statistical Prototypes (MSP) is a modification of a standard minimum distance classification scheme that generates multiple prototypes per class using a modified greedy heuristic. Empirical comparison of MSP with other well-known learning algorithms shows MSP to be a robust algorithm that uses a very simple premise to produce good generalization and achieve parsimonious hypothesis representation.
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