Prediction Model of Non-Iterative Least Squares Support Vector Machines Based on Quadratic Renyi-Entropy

Abstract:

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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.

Info:

Periodical:

Key Engineering Materials (Volumes 474-476)

Edited by:

Garry Zhu

Pages:

967-972

DOI:

10.4028/www.scientific.net/KEM.474-476.967

Citation:

G. H. Zhao "Prediction Model of Non-Iterative Least Squares Support Vector Machines Based on Quadratic Renyi-Entropy", Key Engineering Materials, Vols. 474-476, pp. 967-972, 2011

Online since:

April 2011

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Price:

$35.00

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