multiple statistical prototypes, MSP, learning algorithms
Multiple Statistical Prototypes (MSP) is a modification of a standard minimum distance classification scheme that generates muItiple 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.
Original Publication Citation
Ventura, D. and Martinez, T. R., "Using Multiple Statistical Prototypes to Classify Continuously Valued Data", Proceedings of the 2nd International Symposium on Neuroinformatics and Neurocomputers, pp. 238-245, 1995.
BYU ScholarsArchive Citation
Martinez, Tony R. and Ventura, Dan A., "Using Multiple Statistical Prototypes to Classify Continuously Valued Data" (1995). All Faculty Publications. 1160.
Physical and Mathematical Sciences
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