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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：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：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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公开(公告)号：US20190304298A1
公开(公告)日：2019-10-03
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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公开(公告)号：US20170147675A1
公开(公告)日：2017-05-25
申请号：US14945853
申请日：2015-11-19
发明人： Sier Han , Zhiyuan Wang , Ji Hui Yang , A Peng Zhang , Xueying Zhang , Xiu Fang Zhu
IPC分类号： G06F17/30
CPC分类号： G06F16/35
摘要： Refining cluster definition: (i) receiving data items, each characterized by values respectively corresponding to a set of dimension(s); (ii) receiving initial cluster identification that divides the set of data items into multiple initial clusters; (iii) determining a distribution curve, with respect to a first dimension, of data items of a first initial cluster; (iv) determining a distribution curve, with respect to the first dimension, of data items of a second initial cluster; and (v) determining a first-dimension-first-cluster-second-cluster cut-off value such that the following two proportions are substantially equal: (a) a proportion of the area under the first distribution curve and below the first-dimension-first-cluster-second-cluster cut-off value to the total area under the first distribution curve, and (b) a proportion of the area under the second distribution curve and above the first-dimension-first-cluster-second-cluster cut-off value to the total area under the second distribution curve.
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公开(公告)号：US12093838B2
公开(公告)日：2024-09-17
申请号：US17027688
申请日：2020-09-21
发明人： Jing Xu , Si Er Han , Xue Ying Zhang , Steven George Barbee , Ji Hui Yang
IPC分类号： G06N5/01 , G06F17/18 , G06F18/22 , G06F18/2413 , G06N7/01
CPC分类号： G06N5/01 , G06F17/18 , G06F18/22 , G06F18/2413 , G06N7/01
摘要： Embodiments of the present disclosure relate to a method, system, and computer program product for efficient execution of a decision tree. According to the method, respective target values of a plurality of attributes of a target entity are obtained. Representations of a plurality of leaf nodes of a decision tree are obtained. Each of the representations indicates respective statistic values of a plurality of attributes of historical entities and a statistic prediction result determined from historical prediction results output at a respective one of the plurality of leaf nodes for the historical entities. Distance measures between the target entity and the plurality of leaf nodes are determined based on the target values and the statistic values indicated by the representations of the plurality of leaf nodes. A target prediction result for the target entity is determined based on the distance measures and the statistic prediction results of the historical entities.
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公开(公告)号：US11966340B2
公开(公告)日：2024-04-23
申请号：US17654965
申请日：2022-03-15
发明人： Long Vu , Bei Chen , Xuan-Hong Dang , Peter Daniel Kirchner , Syed Yousaf Shah , Dhavalkumar C. Patel , Si Er Han , Ji Hui Yang , Jun Wang , Jing James Xu , Dakuo Wang , Gregory Bramble , Horst Cornelius Samulowitz , Saket K. Sathe , Wesley M. Gifford , Petros Zerfos
IPC分类号： G06F12/0871 , G06N20/00
CPC分类号： G06F12/0871 , G06N20/00 , G06F2212/604
摘要： To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.
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