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.
描述了实现过程采用的重要数据结构、针对区分服务支持设计的流程以及网络调度算法。给出了API接口使用规范。
Key data structure, program flows designed for promotion of differentiated classes of service, scheduling algorithms and specification of API have been narrated in addition.
更改编码算法进行区分旧从新的数据更容易。
Changing the encoding algorithm made distinguishing old from new data easier.
针对这一问题，提出了一种基于候选断点区分矩阵的数据离散化算法。
In order to overcome the problem, a new method of data discretization based on candidate cuts discernibility matrix is presented.
本文算法使得所提特征之间相互无关，这样降低了数据冗余，同时考虑到类别信息，使得投影后的类间区分度加强了。
The algorithm proposed here not only imposes an uncorrelated constraint to reduce data redundancy, but also utilizes the class information and the interclass separability after projection is enhanced.
该模型首先利用SVM算法对全部的输人数据进行区分，然后将其认为的合法数据集用KMO算法分类，以该结果作为决策模块的输入并做出最终的响应。
In this model, firstly use the SVM algorithm to filter all the input data, then the considered legitimate data is classified with KMO, so the decision-making module can respond the final input data.
该模型首先利用SVM算法对全部的输人数据进行区分，然后将其认为的合法数据集用KMO算法分类，以该结果作为决策模块的输入并做出最终的响应。
In this model, firstly use the SVM algorithm to filter all the input data, then the considered legitimate data is classified with KMO, so the decision-making module can respond the final input data.
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