专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明申请

WO2010017300A1 SYSTEMS AND METHODS EMPLOYING COOPERATIVE OPTIMIZATION-BASED DIMENSIONALITY REDUCTION 审中-公开
标题翻译：基于合作优化的尺寸减少的系统和方法
公开(公告)号：WO2010017300A1
公开(公告)日：2010-02-11
申请号：PCT/US2009/052860
申请日：2009-08-05
申请人： HALLIBURTON ENERGY SERVICES, INC. , CHEN, Dingding , HAMID, Syed , DIX, Michael, C.
发明人： CHEN, Dingding , HAMID, Syed , DIX, Michael, C.
IPC分类号： G01V1/40
CPC分类号： G01V1/34 , G06N3/126 , G06T11/206
摘要： Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.
摘要翻译：只要在维度降低过程中保留数据集的基本信息，尺寸减小系统和方法便于高维数据集的可视化，理解和解释。在一些所公开的实施例中，使用聚类，集群内核的低维度坐标的进化计算，核心位置的粒子群优化以及基于内核映射的神经网络的训练来实现维数降低。为进化计算和粒子群优化选择的适应度函数被设计为保留核心距离以及被认为对所公开技术的当前应用有用的任何其它信息，例如与将来的测量将要预测的变量的线性相关。各种错误措施是合适的，可以使用。

IPRDB

热门服务

关于我们

友情链接

联系方式