-
公开(公告)号：US20230017215A1
公开(公告)日：2023-01-19
申请号：US17935120
申请日：2022-09-25
发明人： Xudong Sun , Yanfang ZHANG , Qinglong CHANG , Liang ZHANG
IPC分类号： G06F30/20
摘要： A modeling method and an apparatus are disclosed. The method includes: obtaining a first data set of a first indicator, and determining, based on the first data set, a second indicator similar to the first indicator; and determining a first model based on one or more second models associated with the second indicator. The first model is used to detect a status of the first indicator, and the status of the first indicator includes an abnormal state or a normal state. The second models are used to detect a status of the second indicator, and the status of the second indicator includes an abnormal state or a normal state.
-
公开(公告)号：US20220121994A1
公开(公告)日：2022-04-21
申请号：US17562724
申请日：2021-12-27
发明人： Xudong Sun , Yanfang Zhang , Liang Zhang , Shucheng Liu , Yali Wang
摘要： A method and an apparatus for implementing model training, and a computer storage medium are disclosed, and belong to the field of machine learning. When a machine learning model deteriorates, an analysis device first obtains validity information of a first feature set, where the first feature set includes a plurality of features used for training to obtain the machine learning model, the validity information includes a validity score of each feature in the first feature set, and a validity score of a feature is negatively related to correlation of the feature with another feature in the first feature set. Then an invalid feature in the first feature set is determined based on the validity information. A second feature set that does not include the invalid feature is finally generated, where the second feature set is used to retrain the machine learning model.
-
3.发明公开公开(公告)号：US20230306311A1
公开(公告)日：2023-09-28
申请号：US18325533
申请日：2023-05-30
发明人： Yanfang Zhang , Qinglong Chang , Jikun Ding , Liang Zhang , Xudong Sun
IPC分类号： G06N20/00
CPC分类号： G06N20/00
摘要： A federated learning method includes: each second device in a plurality of second devices first obtains data distribution information and sends the data distribution information to a first device. The first device receives the data distribution information from the plurality of second devices participating in federated learning. The first device selects a matched federated learning policy based on the data distribution information. The first device sends a parameter reporting policy corresponding to the federated learning to at least one second device in the plurality of second devices. A second device that receives the parameter reporting policy is configured to obtain second gain information based on the parameter reporting policy and a current training sample, and the second gain information is for obtaining a second model of the second device.
-
公开(公告)号：US20230169096A1
公开(公告)日：2023-06-01
申请号：US18150505
申请日：2023-01-05
发明人： Qinglong Chang , Xinyu Hu , Yanfang Zhang , Xudong Sun , Liang Zhang
IPC分类号： G06F16/28 , G06F18/23213
CPC分类号： G06F16/285 , G06F18/23213
摘要： A sample data annotation system includes an edge node and a central node. The edge node obtains a key feature of sample data, determines, based on the key feature, whether the sample data is unknown sample data, when the sample data is unknown sample data, performs annotation processing on the sample data to obtain a first annotation result, and sends the first annotation result to the central node. The central node receives the first annotation result, and determines whether the first annotation result indicates successful annotation; and when the first annotation result indicates that the unknown sample data is successfully annotated, performs consistency processing on the first annotation result to obtain a second annotation result, or when the annotation result indicates that the unknown sample data fails to be annotated, performs annotation processing on the unknown sample data to obtain a third annotation result.
-
5.发明公开公开(公告)号：US20230146912A1
公开(公告)日：2023-05-11
申请号：US18148305
申请日：2022-12-29
发明人： Yanfang Zhang , Xudong Sun , Qinglong Chang , Liang Zhang
摘要： A method, an apparatus, and a computing device for constructing a prediction model, and a storage medium are disclosed, and relate to the field of artificial intelligence technologies. The method includes: obtaining, based on a target dataset of a target prediction scenario and/or scenario information of the target prediction scenario, model search space corresponding to the target prediction scenario; performing model training based on the target dataset and models and hyperparameters that are included in the model search space, to obtain trained prediction models; and obtaining, based on evaluation results of the trained prediction models, a prediction model corresponding to the target prediction scenario. Efficiency of constructing the prediction model can be improved.
-
公开(公告)号：US20220179884A1
公开(公告)日：2022-06-09
申请号：US17683973
申请日：2022-03-01
发明人： Yanfang Zhang , Li Xue , Xudong Sun , Qinglong Chang , Lei Luo
摘要： A label determining method includes: obtaining a target feature vector of a first time series, where a time series is a set of a group of data arranged in a time sequence; obtaining a similarity between the target feature vector and a reference feature vector in a reference feature vector set, where the reference feature vector is a feature vector of a second time series with a determined label; and when a similarity between the target feature vector and a first reference feature vector is greater than a similarity threshold, determining that a label corresponding to the first reference feature vector is a label of the first time series, where the first reference feature vector is a reference feature vector in the reference feature vector set.
-
-
-
-
-
搜索结果
6 条记录 (耗时 0.021 秒)
