Reconstruction of dependence based on Bayesian correction of a collection of pattern recognition algorithms
V. V. Ryazanov, Yu. I. Tkachev
Dorodnicyn Computing Center, Russian Academy of Sciences, ul. Vavilova 40, Moscow, 119333 Russia
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.
regression, logical class regularities, Bayes' formula, precedent-based recognition, prediction, group decisions, estimate evaluation algorithms.
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Computational Mathematics and Mathematical Physics, 2010, 50:9, 1605–1614
V. V. Ryazanov, Yu. I. Tkachev, “Reconstruction of dependence based on Bayesian correction of a collection of pattern recognition algorithms”, Zh. Vychisl. Mat. Mat. Fiz., 50:9 (2010), 1687–1696; Comput. Math. Math. Phys., 50:9 (2010), 1605–1614
Citation in format AMSBIB
\by V.~V.~Ryazanov, Yu.~I.~Tkachev
\paper Reconstruction of dependence based on Bayesian correction of a collection of pattern recognition algorithms
\jour Zh. Vychisl. Mat. Mat. Fiz.
\jour Comput. Math. Math. Phys.
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