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59 条记录 (耗时 0.03 秒)

    Active feature probing using data augmentation
    1.
    发明授权
    Active feature probing using data augmentation 有权
    使用数据增加的主动特征探测

    公开(公告)号：US07958064B2

    公开(公告)日：2011-06-07

    申请号：US11869892

    申请日：2007-10-10

    申请人： Shenghuo Zhu ,  Yihong Gong

    发明人： Shenghuo Zhu ,  Yihong Gong

    IPC分类号： G06F15/18

    CPC分类号： H04L41/5061 ,  H04L41/5074

    摘要： Systems and methods are disclosed that performs active feature probing using data augmentation. Active feature probing is a means of actively gathering information when the existing information is inadequate for decision making. The data augmentation technique generates factitious data which complete the existing information. Using the factitious data, the system is able to estimate the reliability of classification, and determine the most informative feature to probe, then gathers the additional information. The features are sequentially probed until the system has adequate information to make the decision.

    摘要翻译： 公开了使用数据增加来执行主动特征探测的系统和方法。 主动特征探测是当现有信息不足以决策时积极收集信息的一种手段。 数据增加技术生成完成现有信息的数据。 使用事实数据，系统能够估计分类的可靠性，并确定最具信息性的特征进行探测，然后收集附加信息。 这些功能被依次探测，直到系统有足够的信息作出决定。

    Systems and methods for generating predictive matrix-variate T models
    2.
    发明授权
    Systems and methods for generating predictive matrix-variate T models 有权
    用于生成预测矩阵变量T模型的系统和方法

    公开(公告)号：US07870083B2

    公开(公告)日：2011-01-11

    申请号：US11869886

    申请日：2007-10-10

    申请人： Shenghuo Zhu ,  Kai Yu ,  Yihong Gong

    发明人： Shenghuo Zhu ,  Kai Yu ,  Yihong Gong

    IPC分类号： G06F15/18 ,  G06F15/00

    CPC分类号： G06N99/005 ,  G06K9/62

    摘要： Systems and methods are disclosed to predict one or more missing elements from a partially-observed matrix by receiving one or more user item ratings; generating a model parameterized by matrices U, S, V; applying the model to display an item based on one or more predicted missing elements; and applying the model at run-time and determining UiTSVj.

    摘要翻译： 公开了系统和方法以通过接收一个或多个用户项目评级来从部分观察到的矩阵中预测一个或多个缺失元素; 生成由矩阵U，S，V参数化的模型; 应用所述模型以基于一个或多个预测的缺失元素显示项目; 并在运行时应用模型并确定UiTSVj。

    SYSTEMS AND METHODS FOR RESOLUTION-INVARIANT IMAGE REPRESENTATION
    4.
    发明申请
    SYSTEMS AND METHODS FOR RESOLUTION-INVARIANT IMAGE REPRESENTATION 有权
    用于分辨率 - 不变图像表示的系统和方法

    公开(公告)号：US20100124383A1

    公开(公告)日：2010-05-20

    申请号：US12469098

    申请日：2009-05-20

    申请人： Jinjun Wang ,  Shenghuo Zhu ,  Yihong Gong

    发明人： Jinjun Wang ,  Shenghuo Zhu ,  Yihong Gong

    IPC分类号： G06K9/32

    CPC分类号： G06T3/4053

    摘要： Systems and methods are disclosed for generating super resolution images by building a set of multi-resolution bases from one or more training images; estimating a sparse resolution-invariant representation of an image, and reconstructing one or more missing patches at any resolution level.

    摘要翻译： 公开了用于通过从一个或多个训练图像构建一组多分辨率基底来产生超分辨率图像的系统和方法; 估计图像的稀疏分辨率不变表示，以及在任何分辨率级别重建一个或多个丢失的斑点。

    Systems and methods for resolution-invariant image representation
    6.
    发明授权
    Systems and methods for resolution-invariant image representation 有权
    用于分辨率不变图像表示的系统和方法

    公开(公告)号：US08538200B2

    公开(公告)日：2013-09-17

    申请号：US12469098

    申请日：2009-05-20

    申请人： Jinjun Wang ,  Shenghuo Zhu ,  Yihong Gong

    发明人： Jinjun Wang ,  Shenghuo Zhu ,  Yihong Gong

    IPC分类号： G06K9/32

    CPC分类号： G06T3/4053

    摘要： Systems and methods are disclosed for generating super resolution images by building a set of multi-resolution bases from one or more training images; estimating a sparse resolution-invariant representation of an image, and reconstructing one or more missing patches at any resolution level.

    摘要翻译： 公开了用于通过从一个或多个训练图像构建一组多分辨率基底来产生超分辨率图像的系统和方法; 估计图像的稀疏分辨率不变表示，以及在任何分辨率级别重建一个或多个丢失的斑点。

    Monitoring driving safety using semi-supervised sequential learning
    7.
    发明授权
    Monitoring driving safety using semi-supervised sequential learning 有权
    使用半监督顺序学习监测驾驶安全

    公开(公告)号：US07860813B2

    公开(公告)日：2010-12-28

    申请号：US12184221

    申请日：2008-07-31

    申请人： Jinjun Wang ,  Shenghuo Zhu ,  Yihong Gong

    发明人： Jinjun Wang ,  Shenghuo Zhu ,  Yihong Gong

    IPC分类号： G06F15/18

    CPC分类号： G09B9/052

    摘要： A computer-implemented method and system for predicting operation risks of a vehicle. The method and system obtains a training data stream of vehicular dynamic parameters and logging crash time instances; partitions the data stream into units representing dimension vectors, labels the units that overlap the crash time instances as most dangerous; labels the units, which are furthest from the units that are labeled as most dangerous, as most safe; propagates the most dangerous and the most safe labeling information of the labeled units to units which are not labeled; estimates parameters of a danger-level function using the labeled and unlabeled units; and applies the danger-level function to an actual data stream of vehicular dynamic parameters to predict the operation risks of the vehicle.

    摘要翻译： 一种用于预测车辆操作风险的计算机实现的方法和系统。 该方法和系统获取车辆动态参数和日志崩溃时间实例的训练数据流; 将数据流划分为表示维度向量的单位，将与崩溃时间实例重叠的单位标记为最危险的; 标记与最危险的单位最远的单位，最安全; 将标记单位的最危险和最安全的标签信息传播到未标记的单位; 使用标记和未标记的单位估计危险度函数的参数; 并将危险度函数应用于车辆动态参数的实际数据流，以预测车辆的运行风险。

    SOCIAL NETWORK ANALYSIS WITH PRIOR KNOWLEDGE AND NON-NEGATIVE TENSOR FACTORIZATION
    8.
    发明申请
    SOCIAL NETWORK ANALYSIS WITH PRIOR KNOWLEDGE AND NON-NEGATIVE TENSOR FACTORIZATION 有权
    具有先前知识和非负性传感器参数的社会网络分析

    公开(公告)号：US20100185578A1

    公开(公告)日：2010-07-22

    申请号：US12469043

    申请日：2009-05-20

    申请人： Yun Chi ,  Shenghuo Zhu ,  Koji Hino ,  Yihong Gong

    发明人： Yun Chi ,  Shenghuo Zhu ,  Koji Hino ,  Yihong Gong

    IPC分类号： G06N7/02 ,  G06N5/02 ,  G06F17/10

    CPC分类号： G06Q30/02

    摘要： Systems and methods are disclosed to analyze a social network by generating a data tensor from social networking data; applying a non-negative tensor factorization (NTF) with user prior knowledge and preferences to generate a core tensor and facet matrices; and rendering information to social networking users based on the core tensor and facet matrices.

    摘要翻译： 公开了通过从社交网络数据生成数据张量来分析社交网络的系统和方法; 应用具有用户先验知识和偏好的非负张量因子分解（NTF）来生成核心张量和小平面矩阵; 并基于核心张量和面矩阵将信息呈现给社交网络用户。

    Social network analysis with prior knowledge and non-negative tensor factorization
    10.
    发明授权
    Social network analysis with prior knowledge and non-negative tensor factorization 有权
    社会网络分析与先验知识和非负张量分解

    公开(公告)号：US08346708B2

    公开(公告)日：2013-01-01

    申请号：US12469043

    申请日：2009-05-20

    申请人： Yun Chi ,  Shenghuo Zhu ,  Koji Hino ,  Yihong Gong

    发明人： Yun Chi ,  Shenghuo Zhu ,  Koji Hino ,  Yihong Gong

    IPC分类号： G06F9/44 ,  G06N7/02 ,  G06N7/06

    CPC分类号： G06Q30/02

    摘要： Systems and methods are disclosed to analyze a social network by generating a data tensor from social networking data; applying a non-negative tensor factorization (NTF) with user prior knowledge and preferences to generate a core tensor and facet matrices; and rendering information to social networking users based on the core tensor and facet matrices.

    摘要翻译： 公开了通过从社交网络数据生成数据张量来分析社交网络的系统和方法; 应用具有用户先验知识和偏好的非负张量因子分解（NTF）来生成核心张量和小平面矩阵; 并基于核心张量和面矩阵将信息呈现给社交网络用户。