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    Stereoscopic learning for classification
    1.
    发明授权
    Stereoscopic learning for classification 有权

    公开(公告)号：US10528889B2

    公开(公告)日：2020-01-07

    申请号：US15081278

    申请日：2016-03-25

    Applicant: Futurewei Technologies, Inc.

    Inventor： Jiangsheng Yu ,  Hui Zang

    IPC: G06N20/00 ,  G06F16/28

    Abstract: A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers.

    APPARATUS AND METHOD FOR DATASET MODEL FITTING USING A CLASSIFYING ENGINE
    3.
    发明申请
    APPARATUS AND METHOD FOR DATASET MODEL FITTING USING A CLASSIFYING ENGINE 审中-公开

    公开(公告)号：US20180089581A1

    公开(公告)日：2018-03-29

    申请号：US15277970

    申请日：2016-09-27

    Applicant: Futurewei Technologies, Inc.

    Inventor： Hui Zang ,  Yiming Kong

    IPC: G06N99/00 ,  G06N5/02

    CPC classification number: G06N20/00 ,  G06N7/005

    Abstract: An apparatus and method are provided for dataset model fitting, and using a classifying engine to identify a statistical distribution for the dataset. The dataset classifying engine is configured to calculate a characterization function that represents a dataset and compute a feature vector for the dataset, where the feature vector encodes slope value changes corresponding to the characterization function. The dataset classifying engine receives a classification model and applies the classification model to the feature vector to identify a statistical distribution for the dataset.

    STEREOSCOPIC LEARNING FOR CLASSIFICATION
    6.
    发明申请
    STEREOSCOPIC LEARNING FOR CLASSIFICATION 审中-公开

    公开(公告)号：US20170278013A1

    公开(公告)日：2017-09-28

    申请号：US15081278

    申请日：2016-03-25

    Applicant: Futurewei Technologies, Inc.

    Inventor： Jiangsheng Yu ,  Hui Zang

    IPC: G06N99/00 ,  G06F17/30

    CPC classification number: G06N20/00 ,  G06F16/283 ,  G06F16/285

    Abstract: A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers.

    Knowledge network platform
    7.
    发明授权
    Knowledge network platform 有权

    公开(公告)号：US11100406B2

    公开(公告)日：2021-08-24

    申请号：US15473232

    申请日：2017-03-29

    Applicant: Futurewei Technologies, Inc.

    Inventor： Luhui Hu ,  Hui Zang ,  Ziang Hu

    IPC: G06N5/02 ,  G06N5/04 ,  G06N3/04 ,  G06N20/00 ,  H04L29/08 ,  G06N20/20 ,  G06N20/10 ,  G06N7/00

    Abstract: An apparatus and method are provided for a managed knowledge network platform (KNP). Model dissimilarity values for model pairs are obtained, each model pair including a first model of a plurality of models in a KNP and a different model in the plurality of models. Path lengths between a first model node of a plurality of model nodes in the KNP and each one of other model nodes are computed, where the first model node represents the first model and the first model node is connected to a first user node of a plurality of user nodes representing users of the KNP. At least one of the different models is selected based on the model dissimilarity values and the path lengths. A recommendation that includes the at least one model is generated for a first user represented by the first user node.

    REVIEW MACHINE LEARNING SYSTEM
    9.
    发明申请
    REVIEW MACHINE LEARNING SYSTEM 审中-公开

    公开(公告)号：US20180276560A1

    公开(公告)日：2018-09-27

    申请号：US15467847

    申请日：2017-03-23

    Applicant: Futurewei Technologies, Inc.

    Inventor： Luhui Hu ,  Hui Zang ,  Ziang Hu

    IPC: G06N99/00

    CPC classification number: G06N20/00

    Abstract: An apparatus and method are provided for review-based machine learning. Included are a non-transitory memory storing instructions and one or more processors in communication with the non-transitory memory. The one or more processors execute the instructions to receive first data, generate a plurality of first features based on the first data, and identify a first set of labels for the first data. A first model is trained using the first features and the first set of labels. The first model is reviewed to generate a second model, by receiving a second set of labels for the first data, and reusing the first features with the second set of labels in connection with training the second model.

    TOPIC BASED INTELLIGENT ELECTRONIC FILE SEARCHING
    10.
    发明申请
    TOPIC BASED INTELLIGENT ELECTRONIC FILE SEARCHING 审中-公开

    公开(公告)号：US20180189307A1

    公开(公告)日：2018-07-05

    申请号：US15395377

    申请日：2016-12-30

    Applicant: Futurewei Technologies, Inc.

    Inventor： Jiangsheng Yu ,  Hui Zang

    IPC: G06F17/30

    CPC classification number: G06F16/148 ,  G06F16/13 ,  G06F16/156 ,  G06F16/221 ,  G06F16/24578

    Abstract: An apparatus comprises a non-transitory memory that stores a query for of electronic files and instructions. One or more processors execute the instructions to represent the plurality of electronic files as a plurality of column vectors. Each entry in a column vector represents a frequency of a word used in an electronic file. The query is represented as a query vector with each entry representing a frequency of a word used in the query. A topic space is formed from the plurality of column vectors. Each column vector in the term-document-matrix is projected into the topic space to obtain new representations of the plurality of electronic files. The query vector is projected into the topic space to obtain a new representation of the query. A similarity score is calculated between each representation of the electronic files with the representation of the query to obtain a plurality of similarity scores.

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