信息与控制2012，Vol.41Issue(5)：617-621,5.DOI:10.3724/SP.J.1219.2012.00617
代价约束多核最小二乘支持向量机及其应用
Cost-Constraint Based Multiple Kernel Least Squares Support Vector Machine and Its Application
摘要
Abstract
Considering the neglect of kernel function cost and lack of sparsity of multiple kernel least squares support vector machine (MK-LSSVM), a cost-constraint based multiple kernel least squares vector machine with sparsity is proposed. The primal optimal problem of MK-LSSVM is converted into second-order cone programming, and then the weight of complex kernel function is restricted by introducing cost factors so as to save storage space and computing time of variable quantity. Furthermore, the kernel matrices are reduced by Schmidt orthogonalization theory to lower computational complexity. The total cost of multiple kernel learning can be evaluated according to the number of support vectors and active kernel functions. The simulation results on testing datasets show that the proposed method can achieve the same accuracy as MK-LSSVM by using less support vectors and simpler mixture kernel functions with cheaper consumption and better real-time performance. The cost vaule of froth flotation mineral recovery prediction used the proposed method reduces 27.56.关键词
代价约束/多核学习/最小二乘支持向量机/稀疏性/泡沫浮选/回收率Key words
cost-constraint/ multiple kernel learning/ least squares support vector machine/ sparsity/ froth flotation/ recovery分类
信息技术与安全科学引用本文复制引用
阳春华,任会峰,桂卫华,鄢锋..代价约束多核最小二乘支持向量机及其应用[J].信息与控制,2012,41(5):617-621,5.基金项目
国家杰出青年科学基金资助项目 (61025015) （61025015）
国家自然科学基金重点资助项目(61134006). （61134006）
