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1. 发明申请

US20210287116A1 DISTRIBUTABLE EVENT PREDICTION AND MACHINE LEARNING RECOGNITION SYSTEM 有权
公开(公告)号：US20210287116A1
公开(公告)日：2021-09-16
申请号：US17178798
申请日：2021-02-18
申请人： SAS Institute Inc
发明人： Xu Chen , Jorge Manuel Gomes da Silva , Brett Alan Wujek
IPC分类号： G06N5/04 , G06N20/00
摘要： Data is classified using semi-supervised data. Sparse coefficients are computed using a decomposition of a Laplacian matrix. (B) Updated parameter values are computed for a dimensionality reduction method using the sparse coefficients, the Laplacian matrix, and a plurality of observation vectors. The updated parameter values include a robust estimator of a decomposition matrix determined from the decomposition of the Laplacian matrix. (B) is repeated until a convergence parameter value indicates the updated parameter values for the dimensionality reduction method have converged. A classification matrix is defined using the sparse coefficients and the robust estimator of the decomposition of the Laplacian matrix. The target variable value is determined for each observation vector based on the classification matrix. The target variable value is output for each observation vector of the plurality of unclassified observation vectors and is defined to represent a label for a respective unclassified observation vector.

2. 发明申请

US20200372387A1 DISTRIBUTABLE CLUSTERING MODEL TRAINING SYSTEM 审中-公开
公开(公告)号：US20200372387A1
公开(公告)日：2020-11-26
申请号：US16880551
申请日：2020-05-21
申请人： SAS Institute Inc.
发明人： Yingjian Wang , Xu Chen
IPC分类号： G06N7/00 , G06N20/20
摘要： A computing system trains a clustering model. A responsibility parameter vector is initialized for each observation vector and includes a probability value of a cluster membership. The observation vectors include a plurality of classified observation vectors and a plurality of unclassified observation vectors. (A) Beta distribution parameter values are computed for each cluster. (B) Parameter values are computed for a normal-Wishart distribution for each cluster. (C) Each responsibility parameter vector is updated using the beta distribution parameter values, the parameter values, and a respective observation vector. (D) A convergence parameter value is computed. (E) (A) to (D) are repeated until the computed convergence parameter value indicates the responsibility parameter vector defined for each observation vector of the plurality of unclassified observation vectors is converged. A cluster membership is determined and output for each observation vector using a respective, updated responsibility parameter vector.

3. 发明申请

US20190325267A1 MACHINE LEARNING PREDICTIVE LABELING SYSTEM 有权
公开(公告)号：US20190325267A1
公开(公告)日：2019-10-24
申请号：US16400157
申请日：2019-05-01
申请人： SAS Institute Inc.
发明人： Xu Chen
IPC分类号： G06K9/62 , G06N20/10 , G06F17/18
摘要： A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix. A label is selected for each observation. A mean observation vector and a covariance matrix are computed from the unlabeled observations selected to have each respective label. A number of eigenvalues that have a smallest value is selected from each covariance matrix and used to define a null space for each respective label. A distance value is computed for a distance vector computed to the mean observation vector and projected into the null space associated with the label selected for each respective observation. A diversity rank is determined for each respective observation based on minimum computed distance values. A predefined number of observations having highest values for the diversity rank are included in labeled observations and removed from the unlabeled observations.

4. 发明授权

US10430690B1 Machine learning predictive labeling system 有权
公开(公告)号：US10430690B1
公开(公告)日：2019-10-01
申请号：US16400157
申请日：2019-05-01
申请人： SAS Institute Inc.
发明人： Xu Chen
IPC分类号： G06N5/00 , G06K9/62 , G06F17/18 , G06N20/10
摘要： A computing device predicts an event or classifies an observation. A trained labeling model is executed with unlabeled observations to define a label distribution probability matrix. A label is selected for each observation. A mean observation vector and a covariance matrix are computed from the unlabeled observations selected to have each respective label. A number of eigenvalues that have a smallest value is selected from each covariance matrix and used to define a null space for each respective label. A distance value is computed for a distance vector computed to the mean observation vector and projected into the null space associated with the label selected for each respective observation. A diversity rank is determined for each respective observation based on minimum computed distance values. A predefined number of observations having highest values for the diversity rank are included in labeled observations and removed from the unlabeled observations.

5. 发明授权

US10354204B2 Machine learning predictive labeling system 有权
公开(公告)号：US10354204B2
公开(公告)日：2019-07-16
申请号：US16162794
申请日：2018-10-17
申请人： SAS Institute Inc.
发明人： Xu Chen , Saratendu Sethi
IPC分类号： G06N5/00 , G06N20/00 , G06F17/16 , G06K9/62
摘要： A computing device automatically classifies an observation vector. A label set defines permissible values for a target variable. Supervised data includes a labeled subset that has one of the permissible values. A converged classification matrix is computed based on the supervised data and an unlabeled subset using a prior class distribution matrix that includes a row for each observation vector. Each column is associated with a single permissible value of the label set. A cell value in each column is a likelihood that each associated permissible value of the label set occurs based on prior class distribution information. The value of the target variable is selected using the converged classification matrix. A weighted classification label distribution matrix is computed from the converged classification matrix. The value of the target variable for each observation vector of the plurality of observation vectors is output to a labeled dataset.

6. 发明授权

US11200514B1 Semi-supervised classification system 有权
公开(公告)号：US11200514B1
公开(公告)日：2021-12-14
申请号：US17342825
申请日：2021-06-09
申请人： SAS Institute Inc.
发明人： Xu Chen , Xinmin Wu
IPC分类号： G06N20/00
摘要： Unclassified observations are classified. Similarity values are computed for each unclassified observation and for each target variable value. A confidence value is computed for each unclassified observation using the similarity values. A high-confidence threshold value and a low-confidence threshold value are computed from the confidence values. For each observation, when the confidence value is greater than the high-confidence threshold value, the observation is added to a training dataset and, when the confidence value is greater than the low-confidence threshold value and less than the high-confidence threshold value, the observation is added to the training dataset based on a comparison between a random value drawn from a uniform distribution and an inclusion percentage value. A classification model is trained with the training dataset and classified observations. The trained classification model is executed with the unclassified observations to determine a label assignment.

7. 发明授权

US11010691B1 Distributable event prediction and machine learning recognition system 有权
公开(公告)号：US11010691B1
公开(公告)日：2021-05-18
申请号：US17093917
申请日：2020-11-10
申请人： SAS Institute Inc.
发明人： Xu Chen , Jorge Manuel Gomes da Silva , Brett Alan Wujek
IPC分类号： G06N20/00 , G06F16/28
摘要： Data is classified using semi-supervised data. A decomposition is performed to define a first decomposition matrix that includes first eigenvectors of a weight matrix, a second decomposition matrix that includes second eigenvectors of a transpose of the weight matrix, and a diagonal matrix that includes eigenvalues of the first eigenvectors. Eigenvectors are selected from the first eigenvectors to define a reduced decomposition matrix. A linear transformation matrix is computed as a function of the first decomposition matrix, the reduced decomposition matrix, the diagonal matrix, and a penalty matrix. When a rank of the linear transformation matrix is less than a number of rows of the penalty matrix, a classification matrix is computed by updating a gradient of a cost function. When the rank of the linear transformation matrix is equal to the number of rows of the penalty matrix, the classification matrix is computed using a dual formulation.

8. 发明申请

US20180053071A1 DISTRIBUTED EVENT PREDICTION AND MACHINE LEARNING OBJECT RECOGNITION SYSTEM 审中-公开
公开(公告)号：US20180053071A1
公开(公告)日：2018-02-22
申请号：US15686863
申请日：2017-08-25
申请人： SAS Institute Inc.
发明人： Xu Chen , Tao Wang
IPC分类号： G06K9/62 , G06N99/00 , G06K9/66
CPC分类号： G06K9/6264 , G06F9/48 , G06K9/6259 , G06K9/627 , G06K9/66 , G06N3/0454 , G06N5/003 , G06N99/005
摘要： A computing device predicts occurrence of an event or classifies an object using distributed unlabeled data. Supervised data that includes a labeled subset of a plurality of observation vectors is identified. A total number of threads that will perform labeling of an unlabeled subset of the plurality of observation vectors is determined. The identified supervised data is uploaded to each thread of the total number of threads. Unlabeled observation vectors are randomly select from the unlabeled subset of the plurality of observation vectors to allocate to each thread of the total number of threads. The randomly selected, unlabeled observation vectors are uploaded to each thread of the total number of threads based on the allocation. The value of the target variable for each observation vector of the unlabeled subset of the plurality of observation vectors is determined based on a converged classification matrix and output to a labeled dataset.

9. 发明授权

US09792562B1 Event prediction and object recognition system 有权
公开(公告)号：US09792562B1
公开(公告)日：2017-10-17
申请号：US15335530
申请日：2016-10-27
申请人： SAS Institute Inc.
发明人： Xu Chen , Tao Wang
IPC分类号： G06N99/00 , G06N5/00 , G06N7/00
CPC分类号： G06N99/005 , G06N5/003 , G06N7/005
摘要： A computing device predicts occurrence of an event or classifies an object using semi-supervised data. A label set defines permissible values for a target variable. A value of the permissible values is defined for a subset of observation vectors. A predefined number of times, a distance matrix is computed that defines a distance value between pairs of observation vectors using a distance function and a converged classification matrix; a number of observation vectors is selected that have minimum values for the distance value; a label is requested and a response is received for each of the selected observation vectors; the value of the target variable is updated for each of the selected observation vectors with the received response; and the value of the target variable is determined again by recomputing the converged classification matrix. The value of the target variable for each observation vector is output to a second dataset.

10. 发明授权

US11379685B2 Machine learning classification system 有权
公开(公告)号：US11379685B2
公开(公告)日：2022-07-05
申请号：US17386706
申请日：2021-07-28
申请人： SAS Institute Inc.
发明人： Xu Chen
IPC分类号： G06K9/62 , G06N3/08
摘要： A computing device classifies unclassified observations. A first batch of unclassified observation vectors and a first batch of classified observation vectors are selected. A prior regularization error value and a decoder reconstruction error value are computed. A first batch of noise observation vectors is generated. An evidence lower bound (ELBO) value is computed. A gradient of an encoder neural network model is computed, and the ELBO value is updated. A decoder neural network model and an encoder neural network model are updated. The decoder neural network model is trained. The target variable value is determined for each observation vector of the unclassified observation vectors based on an output of the trained decoder neural network model. The target variable value is output.

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