One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of t...One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships.展开更多
提出一种基于最优样本子集的在线模糊最小二乘支持向量机(least squares support vector machine,LSSVM)混沌时间序列预测方法.算法选择与预测样本时间上以及欧氏距离最近的样本点构成最优样本子集,并采用ε不敏感函数对其进行模糊化处...提出一种基于最优样本子集的在线模糊最小二乘支持向量机(least squares support vector machine,LSSVM)混沌时间序列预测方法.算法选择与预测样本时间上以及欧氏距离最近的样本点构成最优样本子集,并采用ε不敏感函数对其进行模糊化处理,通过模糊LSSVM训练获得预测模型.随着时间窗口的滑动,最优样本子集和预测模型实时更新,模型更新采用分块矩阵方法降低运算复杂度.实验中对时变Ikeda序列进行预测,表明所提出的方法与离线和在线LSSVM相比,训练速度更快,预测精度更高.展开更多
基金Supported by the National Natural Science Foundation of China(60603029)the Natural Science Foundation of Jiangsu Province(BK2007074)the Natural Science Foundation for Colleges and Universities in Jiangsu Province(06KJB520132)~~
文摘One-class support vector machine （OCSVM） and support vector data description （SVDD） are two main domain-based one-class （kernel） classifiers. To reveal their relationship with density estimation in the case of the Gaussian kernel, OCSVM and SVDD are firstly unified into the framework of kernel density estimation, and the essential relationship between them is explicitly revealed. Then the result proves that the density estimation induced by OCSVM or SVDD is in agreement with the true density. Meanwhile, it can also reduce the integrated squared error （ISE）. Finally, experiments on several simulated datasets verify the revealed relationships.
文摘提出一种基于最优样本子集的在线模糊最小二乘支持向量机(least squares support vector machine,LSSVM)混沌时间序列预测方法.算法选择与预测样本时间上以及欧氏距离最近的样本点构成最优样本子集,并采用ε不敏感函数对其进行模糊化处理,通过模糊LSSVM训练获得预测模型.随着时间窗口的滑动,最优样本子集和预测模型实时更新,模型更新采用分块矩阵方法降低运算复杂度.实验中对时变Ikeda序列进行预测,表明所提出的方法与离线和在线LSSVM相比,训练速度更快,预测精度更高.
