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公开(公告)号：US12039011B2
公开(公告)日：2024-07-16
申请号：US17568305
申请日：2022-01-04
发明人： Ke Wei Wei , Jun Wang , Shuang YS Yu , Guang Ming Zhang , Yuan Feng , Yi Dai , Ling Zhuo , Jing Xu
IPC分类号： G06V10/00 , G06F18/21 , G06F18/214 , G06F18/231 , G06N20/20
CPC分类号： G06F18/2148 , G06F18/2178 , G06F18/231 , G06N20/20
摘要： An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.
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公开(公告)号：US11847539B2
公开(公告)日：2023-12-19
申请号：US17370071
申请日：2021-07-08
发明人： Ning Zhang , Yi Shao , Jing Xu , Xue Ying Zhang , Na Zhao
摘要： An approach is provided in which the approach trains a first machine learning model using a set of features corresponding to a set of build blocks. The set of build blocks include at least one dependency build block and at least one artifact package build block. The approach predicts a set of risk values of the set of build blocks using the trained first machine learning model, and marks at least one of the build blocks as a bottleneck in response to comparing the set of risk values against a risk threshold.
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公开(公告)号：US11823077B2
公开(公告)日：2023-11-21
申请号：US16992856
申请日：2020-08-13
发明人： Dong Hai Yu , Jun Wang , Jing Xu , Ji Hui Yang , Xiao Ming Ma , Song Bo
CPC分类号： G06N5/04 , G06F16/285 , G06N20/20
摘要： Provided are a computer-implemented method, a system, and a computer program product. The method comprises extracting features from a plurality of base models in an ensemble model. The plurality of base models are configured to provide respective prediction results. The ensemble model is configured to provide an overall prediction result from the prediction results of the plurality of base models. The features are associated with time performance of the base models. The method further comprises clustering the plurality of base models into a plurality of clusters based on the extracted features. The method further comprises assigning the plurality of base models to a plurality of parallel computation units based on the plurality of clusters.
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公开(公告)号：US11783177B2
公开(公告)日：2023-10-10
申请号：US16574163
申请日：2019-09-18
发明人： Damir Spisic , Jing Xu , Xue Ying Zhang , Xing Wei
IPC分类号： G06N3/08 , G06F18/243 , G06N3/047 , G06N3/048
CPC分类号： G06N3/08 , G06F18/24323 , G06N3/047 , G06N3/048
摘要： A set of classifiable data containing a plurality of classes is ingested. A target class within the plurality of classes is determined. Using the set of classifiable data, an interactive recall rate chart is generated, and the interactive recall rate chart shows a set of target class recall rates against a set of class recall rates for the remainder of the plurality of classes. The interactive recall rate chart is presented to a user. A target class recall rate selection from the set of target class recall rates is received from the user. The set of classifiable data is reclassified, based on the target class recall rate selection.
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公开(公告)号：US20230185879A1
公开(公告)日：2023-06-15
申请号：US17644350
申请日：2021-12-15
发明人： Si Er Han , Xue Ying Zhang , Jing Xu , Xiao Ming Ma , Ji Hui Yang
CPC分类号： G06K9/6228 , G06K9/6261 , G06K9/6262 , G06N20/00
摘要： A computer implemented technique including: splitting data of a historical time series data set into subsets; updating a time series model by backwards data selection to obtain an interim version of the time series model; exploring pattern changes in the new data to obtain new predictors of pattern change; and updating the interim version of the time series model by applying the new predictors of pattern change to obtain an updated version of the time series model.
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公开(公告)号：US20230137184A1
公开(公告)日：2023-05-04
申请号：US17453540
申请日：2021-11-04
发明人： Si Er Han , Ji Hui Yang , Xiao Ming Ma , Jing Xu , Xue Ying Zhang
摘要： A method, system, and computer program product for incremental machine learning for a parametric machine learning model are disclosed. The method may include processing samples comprising historical samples and new samples with an existing parametric machine learning model to obtain at least one prediction residual of each of the samples, wherein the existing parametric machine learning model was trained based on the historical samples. The method may further include clustering the samples based on the at least one prediction residual of each of the samples and features of each of the samples. The method may further include sampling samples in each cluster to ensure that each cluster includes substantially similar number of sampled samples. The method may further include updating the existing parametric machine learning model to obtain an updated parametric machine learning model based on sampled samples in each cluster.
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公开(公告)号：US20230073137A1
公开(公告)日：2023-03-09
申请号：US17447258
申请日：2021-09-09
发明人： Jing Xu , Si Er Han , Xue Ying Zhang , Steven George Barbee , Ji Hui Yang
摘要： A computer implemented method for machine learning model training. A number of processor units creates a cluster model comprising labeled samples and unlabeled samples. The number of processor units identifies cluster information for the labeled samples from the cluster model. The number of processor units adds a set of new features to a set of original features for the labeled samples using the cluster information to form an extended set of features for the labeled samples, wherein the labeled samples with the set of original features and the set of new features form a training data set for training a machine learning model.
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公开(公告)号：US20220101044A1
公开(公告)日：2022-03-31
申请号：US17035816
申请日：2020-09-29
发明人： Jing Xu , Xue Ying Zhang , Si Er Han , Xiao Ming Ma , Ji Hui Yang
摘要： A computer receives a general predictive model and training data. The computer builds a clustering feature tree model to condense the training data into data groups. The computer applies a leave-one-out evaluation method to determine an impact value for each data groups with regard to said general predictive model. The computer identifies a diagnostic category for each data group selected from a list of categories including model-harmful data, model-neutral data, and model-helping data, in accordance with said impact value. The computer removes data in groups labelled as model-harmful from the training data and builds a modified general predictive model based on data in groups labelled as model-neutral or model-helping.
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公开(公告)号：US10949764B2
公开(公告)日：2021-03-16
申请号：US15692963
申请日：2017-08-31
发明人： Yi Shao , Liang Wang , Jing Xu , Jing James Xu
摘要： According to an embodiment, a method, computer system, and computer program product for managing data is provided. The present invention may include accumulating a plurality of predicted outputs according to a data accumulation rule. The plurality of predicted outputs is generated by a predictive model executed by a first system. The present invention may include evaluating, by a second system, an accuracy of the predictive model. Evaluating the accuracy of the predictive model may include determining a degree of difference between the plurality of predicted outputs and information generated during a development stage of the predictive model. The present invention may include determining whether the accuracy of the predictive model has declined by an amount which exceeds a pre-determined threshold. The present invention may include updating the predictive model.
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公开(公告)号：US10692368B2
公开(公告)日：2020-06-23
申请号：US16444124
申请日：2019-06-18
发明人： Jing Xu , Yang Zhang , Jun Wang , Ji Hui Yang , Wen Pei Yu
摘要： A method, system and computer program product are provided for detecting vehicle queue events and managing traffic flow. A computing system recognizes whether a queue event occurred for each vehicle located in an area of interest based on collected vehicle data. The area of interest includes an intersection, and the vehicle data for the each vehicle includes location information and speed information. The location information further includes a distance to an intersection. The computing system identifies differences in queue length among queues in the area of interest based on the vehicle data and determines queue indicators for each of the queues in the area of interest. Based on queue indicators for each of the queues in in the area of interest generated over multiple sampling periods, traffic signal lights at the intersection in the area of interest are managed.
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