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KERNEL PARAMETER SELECTION IN SUPPORT VECTOR DATA DESCRIPTION FOR OUTLIER IDENTIFICATION
KERNEL PARAMETER SELECTION IN SUPPORT VECTOR DATA DESCRIPTION FOR OUTLIER IDENTIFICATION
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机译:支持向量数据描述中的内核参数选择,用于识别外部对象
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摘要
A computing device determines a kernel parameter value for a support vector data description for outlier identification. A first candidate optimal kernel parameter value is computed by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value. The first objective function is defined for a SVDD model using observation vectors to define support vectors. A number of the observation vectors is a predefined sample size. The predefined sample size is incremented by adding a sample size increment. A next candidate optimal kernel parameter value is computed with an incremented number of vectors until a computed difference value is less than or equal to a predefined convergence value.
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