RECOMMENDER SYSTEM WITH FAST MATRIX FACTORIZATION USING INFINITE DIMENSIONS
    11.
    发明申请
    RECOMMENDER SYSTEM WITH FAST MATRIX FACTORIZATION USING INFINITE DIMENSIONS 有权
    使用无限尺寸的快速矩阵拟合的推荐系统

    公开(公告)号:US20090299996A1

    公开(公告)日:2009-12-03

    申请号:US12331346

    申请日:2008-12-09

    IPC分类号: G06F17/30

    CPC分类号: G06F17/30867 G06F17/16

    摘要: Systems and methods are disclosed for generating a recommendation by performing collaborative filtering using an infinite dimensional matrix factorization; generating one or more recommendations using the collaborative filtering; and displaying the recommendations to a user.

    摘要翻译: 公开了用于通过使用无限维矩阵分解进行协同过滤来产生推荐的系统和方法; 使用协同过滤生成一个或多个建议; 并向用户显示建议。

    Active feature probing using data augmentation
    12.
    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

    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
    13.
    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

    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
    14.
    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

    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。

    Recommender system with fast matrix factorization using infinite dimensions
    15.
    发明授权
    Recommender system with fast matrix factorization using infinite dimensions 有权
    推荐系统采用无限维矩阵分解

    公开(公告)号:US08131732B2

    公开(公告)日:2012-03-06

    申请号:US12331346

    申请日:2008-12-09

    IPC分类号: G06F7/00 G06F17/30

    CPC分类号: G06F17/30867 G06F17/16

    摘要: A system is disclosed with a collaborative filtering engine to predict an active user's ratings/interests/preferences on a set of new products/items. The predictions are based on an analysis the database containing the historical data of many users' ratings/interests/preferences on a large set of products/items.

    摘要翻译: 公开了一种具有协作过滤引擎的系统,以预测一组新产品/项目上的主动用户的评级/兴趣/偏好。 这些预测是基于对包含大量产品/项目的许多用户评级/兴趣/偏好的历史数据的数据库的分析。

    Active feature probing using data augmentation
    16.
    发明申请
    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:US20080147852A1

    公开(公告)日:2008-06-19

    申请号:US11869892

    申请日:2007-10-10

    IPC分类号: G06F15/173

    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 resolution-invariant image representation
    17.
    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

    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
    18.
    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

    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。

    Super resolution using gaussian regression
    20.
    发明授权
    Super resolution using gaussian regression 有权
    超分辨率使用高斯回归

    公开(公告)号:US07941004B2

    公开(公告)日:2011-05-10

    申请号:US12112010

    申请日:2008-04-30

    IPC分类号: G06K9/32 G09G5/00

    CPC分类号: G06T3/4053

    摘要: A computer implemented technique for producing super resolution images from ordinary images or videos containing a number of images wherein a number of non-smooth low resolution patches comprising an image are found using edge detection methodologies. The low resolution patches are then transformed using selected basis of a Radial Basis Function (RBF) and Gaussian process regression is used to generate high resolution patches using a trained model. The high resolution patches are then combined into a high resolution image or video.

    摘要翻译: 一种计算机实现的技术,用于从包含多个图像的普通图像或视频产生超分辨率图像,其中使用边缘检测方法找到包括图像的多个非平滑低分辨率补丁。 然后使用径向基函数(RBF)的选定基础来转换低分辨率补丁,并使用高斯过程回归来生成使用训练模型的高分辨率补丁。 然后将高分辨率补丁组合成高分辨率图像或视频。