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- W2381391567 abstract "In order to improve the correct rate of enterprise’s financial distress prediction and reduce the sample number and training time, renyi-entropy and least squares support vector machine (LS-SVM) is applied to the field of financial distress prediction on the basis of the traditional support vector machine prediction model and advances a kind of prediction model which is based on Renyi-entropy and LS-SVM. The paper also independently derives the entropy fit for the financial distress prediction which is in discrete sequence, as well as the expression of support vector machine kernel function. Besides, it also presents the procedures of carrying out the improved algorithm. The experimental results show that the improved algorithm is significantly superior to the traditional LS-SVM as well as the standard support vector machine prediction model, regardless of the training samples number or computing time." @default.
- W2381391567 created "2016-06-24" @default.
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- W2381391567 date "2010-01-01" @default.
- W2381391567 modified "2023-09-24" @default.
- W2381391567 title "Financial distressof prediction model of least squares support vector machine based on renyi-entropy" @default.
- W2381391567 hasPublicationYear "2010" @default.
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