-
公开(公告)号：US09830558B1
公开(公告)日：2017-11-28
申请号：US15185277
申请日：2016-06-17
Applicant: SAS Institute Inc.
Inventor： Arin Chaudhuri , Deovrat Vijay Kakde , Maria Jahja , Wei Xiao , Seung Hyun Kong , Hansi Jiang , Sergiy Peredriy
CPC classification number: G06N99/005 , G06F17/30539 , H04L67/02
Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.
-
公开(公告)号：US20170323221A1
公开(公告)日：2017-11-09
申请号：US15185277
申请日：2016-06-17
Applicant: SAS Institute Inc.
Inventor： Arin Chaudhuri , Deovrat Vijay Kakde , Maria Jahja , Wei Xiao , Seung Hyun Kong , Hansi Jiang , Sergiy Peredriy
CPC classification number: G06N99/005 , G06F17/30539 , H04L67/02
Abstract: A computing device determines an SVDD to identify an outlier in a dataset. First and second sets of observation vectors of a predefined sample size are randomly selected from a training dataset. First and second optimal values are computed using the first and second observation vectors to define a first set of support vectors and a second set of support vectors. A third optimal value is computed using the first set of support vectors updated to include the second set of support vectors to define a third set of support vectors. Whether or not a stop condition is satisfied is determined by comparing a computed value to a stop criterion. When the stop condition is not satisfied, the first set of support vectors is defined as the third set of support vectors, and operations are repeated until the stop condition is satisfied. The third set of support vectors is output.
-
3.发明授权Kernel parameter selection in support vector data description for outlier identification 有权内核参数选择支持向量数据描述异常值识别公开(公告)号：US09536208B1
公开(公告)日：2017-01-03
申请号：US15096552
申请日：2016-04-12
Applicant: SAS Institute Inc.
Inventor： Deovrat Vijay Kakde , Arin Chaudhuri , Seung Hyun Kong , Maria Jahja , Hansi Jiang , Jorge Manuel Gomes da Silva
IPC: G06N99/00
CPC classification number: G06N99/005
Abstract: A computer-readable medium is configured to determine a support vector data description (SVDD). For each of a plurality of values for a kernel parameter, an optimal value of an objective function defined for an SVDD model using a kernel function, a read plurality of data points, and a respective value for the kernel parameter is computed to define a plurality of sets of support vectors. A plurality of first derivative values are computed for the objective function as a difference between the computed optimal values associated with successive values for the kernel parameter. A plurality of second derivative values are computed for the objective function as a difference between the computed plurality of first derivative values associated with successive values for the kernel parameter. A kernel parameter value is selected where the computed plurality of second derivative values first exceeds zero.
Abstract translation: 计算机可读介质被配置为确定支持向量数据描述（SVDD）。 对于内核参数的多个值中的每一个，计算使用内核函数为SVDD模型定义的目标函数的最佳值，读取的多个数据点和用于内核参数的相应值，以定义多个 的支持向量集。 针对目标函数计算多个一阶导数值，作为与内核参数的连续值相关联的计算的最优值之间的差。 针对目标函数计算多个二阶导数值作为与内核参数的连续值相关联的所计算的多个一阶导数值之间的差。 选择核心参数值，其中所计算的多个二阶导数值首先超过零。
-
-
Search Results
3
records
(took 0.012 seconds)
