Improved Pruning Algorithm Using Quadratic Renyi Entropy for LS-SVM Modeling

摘要：

For the loss of sparseness in least squares support vector machine (LS-SVM) model, a new pruning algorithm using Renyi entropy for LS-SVM modeling is presented. The kernel principal component is adopted for data pre-processing, then the training subsets are divided randomly. To solve the problem that the conventional pruning algorithm cannot take full account the location of the Lagrange multiplier, the concept of quadratic Renyi entropy is introduced as the basis of training and pruning in LS-SVM modeling. The results of simulation verify the validity of the algorithms, thus the sparseness and generalization ability of the model can be improved. The presented algorithm can be applied to multiple-output modeling.

关键词： LS-SVM Sparseness Pruning Quadratic Renyi Entropy

作者: WANG Peng YAN Ai-Jun

作者单位: College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 3483-3486

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）