A new optimizing parameter approach of LSSVM multiclass classification model

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• 作者：Kui He Yang (1) ykh86198986@126.com
  Ling Ling Zhao (1) zll@hebust.edu.cn
• 关键词：Least squares support vector machine – ; Kernel function – ; Fibonacci – ; Parameter – ; Multiclass classification
• 刊名：Neural Computing & Applications
• 出版年：2012
• 出版时间：July 2012
• 年：2012
• 卷：21
• 期：5
• 页码：945-955
• 全文大小：408.9 KB
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• 作者单位：1. School of Information, Hebei University of Science and Technology, Shijiazhuang, 050018 China
• ISSN：1433-3058

文摘

The parameter values of kernel function affect classification results to a certain extent. In the paper, a multiclass classification model based on improved least squares support vector machine (LSSVM) is presented. In the model, the non-sensitive loss function is replaced by quadratic loss function, and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. When the LSSVM is used in multiclass classification, it is presented to choose parameter of kernel function on dynamic, which enhances preciseness rate of classification. The Fibonacci symmetry searching algorithm is simplified and improved. The changing rule of kernel function searching region and best shortening step is studied. The best multiclass classification results are obtained by means of synthesizing kernel function searching region and best shortening step. The simulation results show the validity of the model.