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

  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
$32.00
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