用动力系统方法研究了R ay leigh-L iénard混合振子的二次分支到大极限环现象,给出了两个特殊的模型,说明二次分支到大极限环现象的发生可以通过连续改变曲线P(h)和直线l(h)的相对位置来实现.研究表明,二次分支到大极限环发生与否...用动力系统方法研究了R ay leigh-L iénard混合振子的二次分支到大极限环现象,给出了两个特殊的模型,说明二次分支到大极限环现象的发生可以通过连续改变曲线P(h)和直线l(h)的相对位置来实现.研究表明,二次分支到大极限环发生与否以及发生的类型不仅依赖于非线性阻尼项而且还依赖于生成方程.所给出的模型和方法对翼振问题有一定启发作用.展开更多
Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimi...Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.展开更多
文摘用动力系统方法研究了R ay leigh-L iénard混合振子的二次分支到大极限环现象,给出了两个特殊的模型,说明二次分支到大极限环现象的发生可以通过连续改变曲线P(h)和直线l(h)的相对位置来实现.研究表明,二次分支到大极限环发生与否以及发生的类型不仅依赖于非线性阻尼项而且还依赖于生成方程.所给出的模型和方法对翼振问题有一定启发作用.
基金supported by the National Natural Science Foundation of China (No. 60675049)the National Creative Research Groups Science Foundation of China (No. 60721062)the Natural Science Foundation of Zhejiang Province, China (No. Y106414)
文摘Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.
