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    Providing unlabelled training data for training a computational model
    1.
    发明公开
    Providing unlabelled training data for training a computational model 审中-公开

    公开(公告)号：US20230153611A1

    公开(公告)日：2023-05-18

    申请号：US18049138

    申请日：2022-10-24

    Applicant: Nokia Technologies Oy

    Inventor： Akhil MATHUR ,  Chulhong Min ,  Fahim Kawsar

    IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

    CPC classification number: G06N3/08 ,  G06K9/6256 ,  G06N3/0472

    Abstract: Providing unlabelled training data for training a computational model comprises:

    obtaining sets of time-aligned unlabelled data, wherein the sets correspond to different ones of a plurality of sensors;
    marking a first sample, of a first set of the sets, as a positive sample, in dependence on statistical separation information indicating a first statistical similarity of at least a portion of the first set to the at least a portion of the reference set and in dependence on the first sample being time-aligned relative to a reference time;
    marking a second sample, of a second set of the sets, as a negative sample, in dependence on statistical separation information indicating a second, lower statistical similarity, of at least a portion of the second set to the at least a portion of the reference set, and in dependence on the second sample being time-misaligned relative to the reference time.

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