Abstract:This paper presents a method of data resampling inspired by the operation of a variable selection algorithm using Bayesian techniques. It uses covariance calculations to estimate the minimum mean squared error of the training data and to apply a function to calculate the posterior probability to obtain a more significant number of samples to solve a problem. The model was submitted to standard classification tests, and the results were consistent when compared to other traditional literature models. Resumo: Es… Show more
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