ANALYSIS PRIORITY OF OBJECTS FROM CROSS-SECTIONAL VARIANCE
There is disclosed in one example a computing apparatus, including: a processor and a memory; a data store having stored thereon trained models MGLOBAL and MENT, wherein model MGLOBAL includes a clustering model of proximity and prevalence of a first body of computing objects, and MENT includes a cl...
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Zusammenfassung: | There is disclosed in one example a computing apparatus, including: a processor and a memory; a data store having stored thereon trained models MGLOBAL and MENT, wherein model MGLOBAL includes a clustering model of proximity and prevalence of a first body of computing objects, and MENT includes a clustering model of proximity and prevalence of a second body of computing object; and instructions encoded within the memory to instruct the processor to: receive an object under analysis; apply a machine learning model to compute a global variance score between the object under analysis and MGLOBAL; apply the machine learning model to compute an enterprise variance score between the object under analysis and MENT; compute from the global variance score and the enterprise variance score a cross-sectional variance score; and assign the object under analysis an analysis priority according to the cross-sectional variance score. |
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