Invention Publication
- Patent Title: Providing unlabelled training data for training a computational model
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Application No.: US18049138Application Date: 2022-10-24
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Publication No.: US20230153611A1Publication Date: 2023-05-18
- Inventor: Akhil MATHUR , Chulhong Min , Fahim Kawsar
- Applicant: Nokia Technologies Oy
- Applicant Address: FI Espoo
- Assignee: Nokia Technologies Oy
- Current Assignee: Nokia Technologies Oy
- Current Assignee Address: FI Espoo
- Priority: FI 216168 2021.11.12
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06K9/62
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.
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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