Geodesic
Reconstruction of dependence based on Bayesian correction of a collection of pattern recognition algorithms
Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 50 (2010) no. 9, pp. 1687-1696 
V. V. Ryazanov; Yu. I. Tkachev. Reconstruction of dependence based on Bayesian correction of a collection of pattern recognition algorithms. Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 50 (2010) no. 9, pp. 1687-1696. http://geodesic.mathdoc.fr/item/ZVMMF_2010_50_9_a10/
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     author = {V. V. Ryazanov and Yu. I. Tkachev},
     title = {Reconstruction of dependence based on {Bayesian} correction of a collection of pattern recognition algorithms},
     journal = {\v{Z}urnal vy\v{c}islitelʹnoj matematiki i matemati\v{c}eskoj fiziki},
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     url = {http://geodesic.mathdoc.fr/item/ZVMMF_2010_50_9_a10/}
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Voir la notice de l'article provenant de la source Math-Net.Ru

Estimation of dependence of a scalar variable on the vector of independent variables based on a training sample is considered. No a priori conditions are imposed on the form of the function. An approach to the estimation of the functional dependence is proposed based on the solution of a finite number of special classification problems constructed on the basis of the training sample and on the subsequent prediction of the value of the function as a group decision. A statistical model and Bayes’ formula are used to combine the recognition results. A generic algorithm for constructing the regression is proposed for different approaches to the selection of the committee of classification algorithms and to the estimation of their probabilistic characteristics. Comparison results of the proposed approach with the results obtained using other models for the estimation of dependences are presented.

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