Application of HLVQ and G-Prop Neural Networks to the Problem of Bankruptcy Prediction
نویسندگان
چکیده
Predicting the failure of a company is a difficult problem traditionally performed by accounting experts using heuristic rules extracted from experience. In this work we apply HLVQ, a new algorithm to train neural networks, to this problem and compared its results with G-Prop, a neural network optimized with evolutionary algorithms. We show that HLVQ is an efficient alternative for the bankruptcy prediction problem.
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