METHOD AND SYSTEM FOR CLASSIFYING AN INPUT DATA SET USING MULTIPLE DATA REPRESENTATION SOURCE MODES

    公开(公告)号:US20200302157A1

    公开(公告)日:2020-09-24

    申请号:US16791740

    申请日:2020-02-14

    Abstract: A computer-implemented method for classifying an input data set within a data category using multiple data representation modes. The method includes identifying at least a first data representation source mode and a second data representation source mode; classifying the at least first data representation source mode via at least a first data recognition tool and the at least second data representation source mode via at least a second data recognition tool, the classifying including allocating a confidence factor for each data representation source mode in the data category; and combining outputs of the classifying into a single output confidence score by using a weighted fusion of the allocated confidence factors.

    METHOD AND SYSTEM FOR CLASSIFYING AN INPUT DATA SET WITHIN A DATA CATEGORY USING MULTIPLE DATA RECOGNITION TOOLS

    公开(公告)号:US20200302169A1

    公开(公告)日:2020-09-24

    申请号:US16791675

    申请日:2020-02-14

    Abstract: A computer-implemented method and system are disclosed for classifying an input data set within a data category using multiple data recognition tools. The method includes identifying at least a first attribute and a second attribute of the data category; classifying the at least first attribute via at least a first data recognition tool and the at least second attribute via at least a second data recognition tool, the classifying including: allocating a confidence factor for each of the at least first and second attributes that indicates a presence of each of the at least first and second attributes in the input data set; and combining outputs of the classifying into a single output confidence score by using a weighted fusion of the allocated confidence factors.

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