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Conference Paper (international conference)

Boosting in probabilistic neural networks

Grim Jiří, Pudil Pavel, Somol Petr

: Proceedings of the 16th International Conference on Pattern Recognition, p. 136-139 , Eds: Kasturi R., Laurendeau D., Suen C.

: IEEE Computer Society, (Los Alamitos 2002)

: International Conference on Pattern Recognition /16./, (Québec City, CA, 11.08.2002-15.08.2002)

: CEZ:AV0Z1075907

: GA402/01/0981, GA ČR, KSK1019101, GA AV ČR

: neural networks, finite mixtures, boosting

: http://library.utia.cas.cz/separaty/historie/grim-boosting in probabilistic neural networks.pdf

(eng): It has been verified in practical experiments that the classification performance can be improved by increasing the weights of misclassified training samples. We prove that in case of maximum-likelihood estimation the weighting of discrete data vectors is asymptotically equivalent to multiplication of the estimated distributions by a positive function. Consequently, the Bayesian decision-making can be made asymptotically invariant with respect to arbitrary weighting of data under certain conditions.

: 09K

: BB