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).
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.
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.
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.
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