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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：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：US11748436B2
公开(公告)日：2023-09-05
申请号：US17483714
申请日：2021-09-23
发明人： Jun Wang , Xue Ying Zhang , Song Bo , Dong Hai Yu , Jing James Xu
IPC分类号： G06F16/957 , G06F16/955 , G06F16/9535
CPC分类号： G06F16/9574 , G06F16/955 , G06F16/9535
摘要： In an approach for detecting web browsing subject-oriented event interactions and intelligently organizing web pages based on insights from important interactions for better exploration and efficient management, a processor extracts time series data associated with a plurality of web browsing events based on browsing historical actions of a user. A processor identifies the subject of each web browsing event. A processor determines major events based on the time series data and subjects of the plurality of web browsing events. A processor organizes the plurality of web browsing events based on subject hierarchy and timeline from the time series data. A processor highlights one or more uniform resource locators based on the subject hierarchy and timeline.
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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：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：US11893499B2
公开(公告)日：2024-02-06
申请号：US16299225
申请日：2019-03-12
发明人： Jing Xu , Rui Wang , Xiao Ming Ma , Ji Hui Yang , Xue Ying Zhang , Jing James Xu , Si Er Han
摘要： Automated development and training of deep forest models for analyzing data by growing a random forest of decision trees using data, determining Out-of-bag (OOB) predictions for the forest, appending the OOB predictions to the data set, and growing an additional forest using the data set including the appended OOB predictions, and combining the output of the additional forest, then utilizing the model to classify data outside the training data set.
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公开(公告)号：US20230394326A1
公开(公告)日：2023-12-07
申请号：US17804935
申请日：2022-06-01
发明人： Si Er Han , Xue Ying Zhang , Xiao Ming Ma , Wen Pei Yu , Jing Xu , Jing James Xu , Rui Wang
IPC分类号： G06N5/02
CPC分类号： G06N5/022
摘要： Embodiments of the present disclosure relate to a method, system, and computer program product for predictive models. According to the method, a processor may provide a first list including at least one input variable of a predictive model and a second list including a plurality of variables of the predictive model. For each of input variables in the second list, the processor may determine contribution of the input variable to prediction of the predictive model with respect to the at least one input variable in the first list. The processor may update the first list by moving an input variable in the second list into the first list based on the determined contribution of the plurality of input variables. The processor may render one or more of input variables in the updated first list based on an order of the input variables in the updated first list.
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