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公开(公告)号：US20200151613A1
公开(公告)日：2020-05-14
申请号：US16684627
申请日：2019-11-15
Applicant: Lunit Inc.
Inventor： Dong Geun YOO , Kyung Hyun Paeng , Sung Gyun Park
Abstract: A machine learning method that may reduce an annotation cost and may improve performance of a target model is provided. Some embodiments of the present disclosure may provide a machine learning method performed by a computing device, including: acquiring a training dataset of a first model including a plurality of data samples to which label information is not given; calculating a miss-prediction probability of the first model on the plurality of data samples; configuring a first data sample group by selecting at least one data sample from the plurality of data samples based on the calculated miss-prediction probability; acquiring first label information on the first data sample group; and performing first learning on the first model by using the first data sample group and the first label information.
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公开(公告)号：US10922628B2
公开(公告)日：2021-02-16
申请号：US16684627
申请日：2019-11-15
Applicant: Lunit Inc.
Inventor： Dong Geun Yoo , Kyung Hyun Paeng , Sung Gyun Park
Abstract: A machine learning method that may reduce an annotation cost and may improve performance of a target model is provided. Some embodiments of the present disclosure may provide a machine learning method performed by a computing device, including: acquiring a training dataset of a first model including a plurality of data samples to which label information is not given; calculating a miss-prediction probability of the first model on the plurality of data samples; configuring a first data sample group by selecting at least one data sample from the plurality of data samples based on the calculated miss-prediction probability; acquiring first label information on the first data sample group; and performing first learning on the first model by using the first data sample group and the first label information.
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