针对SVM等各类传统算法耗时过长,无法满足在线要求的问题,提出了一种基于广泛内核核向量机(ECVM)的大规模电力系统在线稳定评估算法。首先基于决策树算法对原始特征量进行特征筛选,然后基于ECVM分类器快速给出电力系统稳定状态的评估结...针对SVM等各类传统算法耗时过长,无法满足在线要求的问题,提出了一种基于广泛内核核向量机(ECVM)的大规模电力系统在线稳定评估算法。首先基于决策树算法对原始特征量进行特征筛选,然后基于ECVM分类器快速给出电力系统稳定状态的评估结果。该算法简化了最小闭包球问题中新球心的计算过程,避免了每次迭代都要解决QP问题,降低了算法的复杂度。在New England 39节点系统和某实际系统下的仿真结果表明了所提算法的优越性,为大规模电力系统的在线稳定评估提供了新思路。展开更多
Ordinal regression is one of the most important tasks of relation learning, and several techniques based on support vector machines （SVMs） have also been proposed for tackling it, but the scalability aspect of these...Ordinal regression is one of the most important tasks of relation learning, and several techniques based on support vector machines （SVMs） have also been proposed for tackling it, but the scalability aspect of these approaches to handle large datasets still needs much of exploration. In this paper, we will extend the recent proposed algorithm Core Vector Machine （CVM） to the ordinal-class data, and propose a new algorithm named as Ordinal-Class Core Vector Machine （OCVM）. Similar with CVM, its asymptotic time complexity is linear with the number of training samples, while the space complexity is independent with the number of training samples. We also give some analysis for OCVM, which mainly includes two parts, the first one shows that OCVM can guarantee that the biases are unique and properly ordered under some situation; the second one illustrates the approximate convergence of the solution from the viewpoints of objective function and KKT conditions. Experiments on several synthetic and real world datasets demonstrate that OCVM scales well with the size of the dataset and can achieve comparable generalization performance with existing SVM implementations.展开更多
文摘针对SVM等各类传统算法耗时过长,无法满足在线要求的问题,提出了一种基于广泛内核核向量机(ECVM)的大规模电力系统在线稳定评估算法。首先基于决策树算法对原始特征量进行特征筛选,然后基于ECVM分类器快速给出电力系统稳定状态的评估结果。该算法简化了最小闭包球问题中新球心的计算过程,避免了每次迭代都要解决QP问题,降低了算法的复杂度。在New England 39节点系统和某实际系统下的仿真结果表明了所提算法的优越性,为大规模电力系统的在线稳定评估提供了新思路。
基金supported by the National High-Tech Research and Development 863 Program of China under Grant No. 2006AA12A106
文摘Ordinal regression is one of the most important tasks of relation learning, and several techniques based on support vector machines （SVMs） have also been proposed for tackling it, but the scalability aspect of these approaches to handle large datasets still needs much of exploration. In this paper, we will extend the recent proposed algorithm Core Vector Machine （CVM） to the ordinal-class data, and propose a new algorithm named as Ordinal-Class Core Vector Machine （OCVM）. Similar with CVM, its asymptotic time complexity is linear with the number of training samples, while the space complexity is independent with the number of training samples. We also give some analysis for OCVM, which mainly includes two parts, the first one shows that OCVM can guarantee that the biases are unique and properly ordered under some situation; the second one illustrates the approximate convergence of the solution from the viewpoints of objective function and KKT conditions. Experiments on several synthetic and real world datasets demonstrate that OCVM scales well with the size of the dataset and can achieve comparable generalization performance with existing SVM implementations.
