Invention Grant
- Patent Title: Generating quantitatively assessed synthetic training data
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Application No.: US16823772Application Date: 2020-03-19
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Publication No.: US11636390B2Publication Date: 2023-04-25
- Inventor: Gabriele Ranco , Moises Noe Sanchez Garcia , Gordon Doyle
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Randy E. Tejeda
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06K9/62 ; G06F17/16 ; G06F17/18

Abstract:
In an approach to generating quantitatively assessed synthetic training data, one or more computer processors identify an initial plurality of clusters in a dataset utilizing a trained classification model and a plurality of associated hyperparameters, wherein the clusters have sufficient density to be represented in a calculated probability distribution. The one or more computer processors generate one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution. The one or more computer processors quantitatively assess the one or more generated synthetic data points.
Public/Granted literature
- US20210295205A1 GENERATING QUANTITATIVELY ASSESSED SYNTHETIC TRAINING DATA Public/Granted day:2021-09-23
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