Abstract
The Hidden Layer Learning Vector Quantization is used to correct the prediction of multilayer perceptrons in classification of high-dimensional data. Corrections are significant for problems with insufficient training data to constrain learning. Our method, HLVQ-C, allows the inclusion of a large number of attributes without compromising the generalization capabilities of the network. The method is applied to the problem of bankruptcy prediction with excellent results.
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Vieira, A., Neves, J.C., Ribeiro, B. (2005). A Method to Improve Generalization of Neural Networks: Application to the Problem of Bankruptcy Prediction. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_100
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DOI: https://doi.org/10.1007/3-211-27389-1_100
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-24934-5
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