Systems and methods for generating predictive matrix-variate T models
    3.
    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。

    Recommender system with fast matrix factorization using infinite dimensions
    4.
    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。

    RECOMMENDER SYSTEM WITH FAST MATRIX FACTORIZATION USING INFINITE DIMENSIONS
    5.
    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。

    Systems and methods for determining personal characteristics
    8.
    发明授权
    Systems and methods for determining personal characteristics 有权
    用于确定个人特征的系统和方法

    公开(公告)号:US08582807B2

    公开(公告)日:2013-11-12

    申请号:US12790979

    申请日:2010-05-31

    IPC分类号: G06K9/00

    摘要: Systems and methods are disclosed for determining personal characteristics from images by generating a baseline gender model and an age estimation model using one or more convolutional neural networks (CNNs); capturing correspondences of faces by face tracking, and applying incremental learning to the CNNs and enforcing correspondence constraint such that CNN outputs are consistent and stable for one person.

    摘要翻译: 公开了用于通过生成基线性别模型和使用一个或多个卷积神经网络(CNN)的年龄估计模型来确定来自图像的个人特征的系统和方法; 通过面部跟踪获取面部对应关系,并将增量学习应用于CNN,并执行对应约束,使得CNN输出对于一个人来说是一致和稳定的。

    SYSTEMS AND METHODS FOR DETERMINING PERSONAL CHARACTERISTICS
    9.
    发明申请
    SYSTEMS AND METHODS FOR DETERMINING PERSONAL CHARACTERISTICS 有权
    用于确定个人特征的系统和方法

    公开(公告)号:US20110222724A1

    公开(公告)日:2011-09-15

    申请号:US12790979

    申请日:2010-05-31

    IPC分类号: G06K9/62

    摘要: Systems and methods are disclosed for determining personal characteristics from images by generating a baseline gender model and an age estimation model using one or more convolutional neural networks (CNNs); capturing correspondences of faces by face tracking, and applying incremental learning to the CNNs and enforcing correspondence constraint such that CNN outputs are consistent and stable for one person.

    摘要翻译: 公开了用于通过生成基线性别模型和使用一个或多个卷积神经网络(CNN)的年龄估计模型来确定来自图像的个人特征的系统和方法; 通过面部跟踪获取面部对应关系,并将增量学习应用于CNN,并执行对应约束,使得CNN输出对于一个人来说是一致和稳定的。

    Fast methods of learning distance metric for classification and retrieval
    10.
    发明授权
    Fast methods of learning distance metric for classification and retrieval 有权
    用于分类和检索的学习距离度量的快速方法

    公开(公告)号:US08873843B2

    公开(公告)日:2014-10-28

    申请号:US13479256

    申请日:2012-05-23

    IPC分类号: G06K9/62

    CPC分类号: G06K9/6276 G06K9/6201

    摘要: A nearest-neighbor-based distance metric learning process includes applying an exponential-based loss function to provide a smooth objective; and determining an objective and a gradient of both hinge-based and exponential-based loss function in a quadratic time of the number of instances using a computer.

    摘要翻译: 基于最近邻的距离度量学习过程包括应用基于指数的损失函数来提供平滑的目标; 以及使用计算机在实例数量的二次时间中确定基于铰链和指数的损失函数的目标和梯度。