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- W2023572520 abstract "By comparing and analysing the model of non-iterative least squares support vector machines (LS-SVM) based on quadratic Renyi-entropy, traditional LS-SVM model and standard support vector machines (SVM) model, this paper concludes whether the number of training samples or computing time,non-iterative LS-SVM model based on quadratic Renyi-entropy are significantly better than the model of traditional LS-SVM and standard SVM model and it also proves the effectiveness of applying the concept of quadratic Renyi-entropy on financial distress prediction. At the same time, by the comparison of different point of 3 years of ST which is from 1to 2, the author concludes the forecast accuracy of 1 year ago before ST, the further distance away from the piont of ST, the lower the prediction accuracy is." @default.
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- W2023572520 date "2011-04-01" @default.
- W2023572520 modified "2023-09-26" @default.
- W2023572520 title "Prediction Model of Non-Iterative Least Squares Support Vector Machines Based on Quadratic Renyi-Entropy" @default.
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- W2023572520 doi "https://doi.org/10.4028/www.scientific.net/kem.474-476.967" @default.
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