计算机工程与应用2012，Vol.48Issue(6)：38-41,4.DOI:10.3778/j.issn.1002-8331.2012.06.012
利用矢量基学习和自适应迭代算法改进LSSVR
Using vector-base learning and adaptive iterative algorithm to improve LSSVR
摘要
Abstract
Combining the advantages of the vector-based learning and adaptive iterative algorithm, an improved weighted Least Squares Support Vector Regression (LSSVR) is proposed to solve the problems of the least squares support vector regression methods, such as lacking of sparsely and robustly. During the training process of algorithm, the vector-base learning and automatic iterative procedures are introduced and a small support vector set can be obtained adaptively. This method can avoid the error accumulation during the iterative processing and improve the sparseness and stability of the algorithm, while the weights are determined by a robust method in order to reduce the effect of the outliers(e.g.resulting from non-Gaussian noise). The experimental results show that the proposed algorithm has a better robust, sparsely of support vector and real-time performance of dynamic modeling.关键词
矢量基/自适应迭代算法/支持向量稀疏性Key words
vector-base/adaptive iterative algorithm/support vector sparsely分类
信息技术与安全科学引用本文复制引用
王鲜芳,杜志勇..利用矢量基学习和自适应迭代算法改进LSSVR[J].计算机工程与应用,2012,48(6):38-41,4.基金项目
国家自然科学基金(No.61173071) （No.61173071）
河南省基础与前沿技术研究计划项目(No.112300410254) （No.112300410254）
河南省科技攻关计划项目(No.112102210412) （No.112102210412）
河南师范大学10博士科研启动课题(521). （521）
