This paper proposes a new anomaly intrusion detection method based on support vector data description (SVDD).
提出了一种基于支持向量数据描述算法的异常检测方法。
Then the input pattern of no-object classes could be rejected by the first support vector domain description (SVDD).
这样对于输入的非目标样本即可利用各类的支持向量域进行拒识或接受处理；
As a type of one-class classification algorithm, Support Vector Data Description (SVDD) was used to distinguish target objects from outlier objects.
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。
In the test on driving axle, the 2d spectrum entropy of vibration signals is extracted as the feature indicator and is input into the SVDD classifier.
试验中，提取驱动桥振动信号频谱的二维谱熵作为特征指标，输入到SVDD分类器。
The test result shows that the SVDD algorithm has high computing efficiency and good effects of classification, meeting the requirements of online detection.
结果表明SVDD算法计算效率高，分类效果好，可以满足在线检测的要求。
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.
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 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.
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。
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。
In the process of test, principal component analysis is used as data preprocessing to extract the feature index from vibration signal statistic features as the input of SVDD classifier.
在测试的过程中，主成分分析作为数据预处理，提取特征指数从振动信号的统计特征作为输入SVDD分类。
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。
The test result shows that after the extraction of PCA, the SVDD classifier distinguished the normal and fault condition finely, and it also has good recognized ability to unknown fault samples.
试验结果表明，PCA对正常和故障样本有较大的区分度，SVDD分类器能很好的分辨出轴承正常和故障状态，并且对未知故障有良好的识别能力。
The test result shows that after the extraction of PCA, the SVDD classifier distinguished the normal and fault condition finely, and it also has good recognized ability to unknown fault samples.
试验结果表明，PCA对正常和故障样本有较大的区分度，SVDD分类器能很好的分辨出轴承正常和故障状态，并且对未知故障有良好的识别能力。
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