Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructe...Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.展开更多
基于Mie光散射理论,开发了石油炼化装置烟气催化剂颗粒浓度在线检测系统,采用了基于最小二乘支持向量机(least square support vector machine,LS-SVM)的软测量技术。利用LS-SVM优良的非线性映射和强大的泛化能力,建立了颗粒浓度的软测...基于Mie光散射理论,开发了石油炼化装置烟气催化剂颗粒浓度在线检测系统,采用了基于最小二乘支持向量机(least square support vector machine,LS-SVM)的软测量技术。利用LS-SVM优良的非线性映射和强大的泛化能力,建立了颗粒浓度的软测量模型,通过LS-SVM的学习和支持向量的自适应更新,实现催化剂颗粒浓度的最佳估计。仿真和实际运行结果表明所开发的监测系统可对烟气催化剂颗粒浓度进行有效测量,其测量精度比经验公式提高4.2倍。展开更多
文摘Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.
文摘基于Mie光散射理论,开发了石油炼化装置烟气催化剂颗粒浓度在线检测系统,采用了基于最小二乘支持向量机(least square support vector machine,LS-SVM)的软测量技术。利用LS-SVM优良的非线性映射和强大的泛化能力,建立了颗粒浓度的软测量模型,通过LS-SVM的学习和支持向量的自适应更新,实现催化剂颗粒浓度的最佳估计。仿真和实际运行结果表明所开发的监测系统可对烟气催化剂颗粒浓度进行有效测量,其测量精度比经验公式提高4.2倍。
