提出了一种基于支持向量数据描述算法的异常检测方法。
This paper proposes a new anomaly intrusion detection method based on support vector data description (SVDD).
为了解决大规模数据中的异常检测问题，提出了基于支持向量数据描述（SVDD）的高效离群数据检测算法。
To efficiently resolve outlier detection problem in large scale data sets, an efficient outlier detection algorithm based on Support Vector Data Description (SVDD) was proposed.
本文以线谱增强类声学故障为研究对象，提出了一种基于支持向量数据描述(SVDD)的故障源识别定位方法。
A new method based on Support Vector Data Description (SVDD) was presented to identify and locate line-spectrum power increase acoustic fault source of system.
数据描述只使用目标集训练样本获得关于目标集的描述，支持向量数据描述（SVDD）是一种有效的数据描述方法。
This paper presented a method that used band selection to reduce operation and weighted support vector data description(WSVDD) method to suppress the interferer effectively.
本论文将基于支持向量数据描述的单值分类方法引入项目风险预警中，提出了基于距离评判和支持向量数据描述的智能预警模型。
A one-class classification method called support vector data description (SVDD) is studied, and an intelligent early warning method based on DET and SVDD is also proposed.
支持向量域数据描述(SVDD)是一种单值分类算法，用于将目标样本与其他非目标样本区分开来。
As a type of one-class classification algorithm, Support Vector Data Description (SVDD) was used to distinguish target objects from outlier objects.
该算法根据支持向量域数据描述本身的特点，利用非高斯性来测量核空间样本接近球形区域分布的程度，并根据此测量结果来优化核参数。
According to the characteristic of SVDD, the proposed algorithm utilizes the non-Gaussian to measure how kernel samples approximate to a spherical area, and then optimize the kernel parameter.
该算法根据支持向量域数据描述本身的特点，利用非高斯性来测量核空间样本接近球形区域分布的程度，并根据此测量结果来优化核参数。
According to the characteristic of SVDD, the proposed algorithm utilizes the non-Gaussian to measure how kernel samples approximate to a spherical area, and then optimize the kernel parameter.
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