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- W2116234824 abstract "In this study, we propose a new Artificial Neural Networks (ANN) training approach that closes the gap between ANN and Data Envel- opment Analysis (DEA), and has the advantage of giving similar re- sults to DEA and being easier to compute. Our method is based on extreme point selection in a bandwidth while determining the training set, and it gives better results than the traditional ANN approach. The proposed approach is tested on simulated data sets with different func- tional forms, sizes, and efficiency distributions. Results s that the proposed ANN approach produces better results in a large number of cases when compared to DEA." @default.
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- W2116234824 date "2010-03-01" @default.
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- W2116234824 title "A New ANN Training Approach for Efficiency Evaluation" @default.
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