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Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation

Author(s): Vu HL; Ng KTW; Richter A; An C;

The use of machine learning techniques in waste management studies is increasingly popular. Recent literature suggests k-fold cross validation may reduce input dataset partition uncertainties and minimize overfitting issues. The objectives are to quantify the benefits of k-fold cross validation for municipal waste disposal prediction and to identify the r ...

Article GUID: 35287077


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