- 专利标题: Method and Device for Training a Machine Learning Algorithm
-
申请号: US17804652申请日: 2022-05-31
-
公开(公告)号: US20220383146A1公开(公告)日: 2022-12-01
- 发明人: Markus Schoeler , Jan Siegemund , Christian Nunn , Yu Su , Mirko Meuter , Adrian Becker , Peet Cremer
- 申请人: Aptiv Technologies Limited
- 申请人地址: BB St. Michael
- 专利权人: Aptiv Technologies Limited
- 当前专利权人: Aptiv Technologies Limited
- 当前专利权人地址: BB St. Michael
- 优先权: EP21176922.9 20210531
- 主分类号: G06N5/02
- IPC分类号: G06N5/02 ; G01S7/41
摘要:
A method is provided for training a machine-learning algorithm which relies on primary data captured by at least one primary sensor. Labels are identified based on auxiliary data provided by at least one auxiliary sensor. A care attribute or a no-care attribute is assigned to each label by determining a perception capability of the primary sensor for the label based on the primary data and based on the auxiliary data. Model predictions for the labels are generated via the machine-learning algorithm. A loss function is defined for the model predictions. Negative contributions to the loss function are permitted for all labels. Positive contributions to the loss function are permitted for labels having a care attribute, while positive contributions to the loss function for labels having a no-care attribute are permitted only if a confidence of the model prediction for the respective label is greater than a threshold.