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    Recovering the structure of sparse markov networks from high-dimensional data
    1.
    发明授权
    Recovering the structure of sparse markov networks from high-dimensional data 有权
    从高维数据恢复稀疏马尔科夫网络的结构

    公开(公告)号：US08326787B2

    公开(公告)日：2012-12-04

    申请号：US12551297

    申请日：2009-08-31

    申请人： Narges Bani Asadi ,  Guillermo A. Cecchi ,  Dimitri Kanevsky ,  Bhuvana Ramabhadran ,  Irina Rish ,  Katya Scheinberg

    发明人： Narges Bani Asadi ,  Guillermo A. Cecchi ,  Dimitri Kanevsky ,  Bhuvana Ramabhadran ,  Irina Rish ,  Katya Scheinberg

    IPC分类号： G06F17/00 ,  G06F7/60 ,  G06F3/00

    CPC分类号： G06N99/005 ,  G06N7/005

    摘要： A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

    摘要翻译： 一种方法，信息处理系统和计算机可读物品的制造模型数据。 接收包括第一组物理世界数据的第一数据集。 基于接收生成与第一数据集相关联的至少一个数据模型。 接收包括第二组物理世界数据的第二数据集。 将第二数据集与至少一个数据模型进行比较。 确定第二数据集由至少一个数据模型建模的概率。 确定概率高于给定阈值。 响应于高于给定阈值的概率，生成基于至少一个数据模型与第二数据集相关联的决定。 概率和决定存储在内存中。 通过用户界面向用户提供概率和决定。

    RECOVERING THE STRUCTURE OF SPARSE MARKOV NETWORKS FROM HIGH-DIMENSIONAL DATA
    2.
    发明申请
    RECOVERING THE STRUCTURE OF SPARSE MARKOV NETWORKS FROM HIGH-DIMENSIONAL DATA 有权
    从高维数据恢复稀疏马尔可夫网络的结构

    公开(公告)号：US20110054853A1

    公开(公告)日：2011-03-03

    申请号：US12551297

    申请日：2009-08-31

    申请人： Narges Bani Asadi ,  Guillermo A. Cecchi ,  Dimitri Kanevsky ,  Bhuvana Ramabhadran ,  Irina Rish ,  Katya Scheinberg

    发明人： Narges Bani Asadi ,  Guillermo A. Cecchi ,  Dimitri Kanevsky ,  Bhuvana Ramabhadran ,  Irina Rish ,  Katya Scheinberg

    IPC分类号： G06F17/10

    CPC分类号： G06N99/005 ,  G06N7/005

    摘要： A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

    摘要翻译： 一种方法，信息处理系统和计算机可读物品的制造模型数据。 接收包括第一组物理世界数据的第一数据集。 基于接收生成与第一数据集相关联的至少一个数据模型。 接收包括第二组物理世界数据的第二数据集。 将第二数据集与至少一个数据模型进行比较。 确定第二数据集由至少一个数据模型建模的概率。 确定概率高于给定阈值。 响应于高于给定阈值的概率，生成基于至少一个数据模型与第二数据集相关联的决定。 概率和决定存储在内存中。 通过用户界面向用户提供概率和决定。

    Recovering the structure of sparse markov networks from high-dimensional data
    3.
    发明授权
    Recovering the structure of sparse markov networks from high-dimensional data 失效
    从高维数据恢复稀疏马尔科夫网络的结构

    公开(公告)号：US08775345B2

    公开(公告)日：2014-07-08

    申请号：US13617558

    申请日：2012-09-14

    申请人： Narges Bani Asadi ,  Guillermo A. Cecchi ,  Dimitri Kanevsky ,  Bhuvana Ramabhadran ,  Irina Rish ,  Katya Scheinberg

    发明人： Narges Bani Asadi ,  Guillermo A. Cecchi ,  Dimitri Kanevsky ,  Bhuvana Ramabhadran ,  Irina Rish ,  Katya Scheinberg

    IPC分类号： G06F17/00 ,  G06F7/60 ,  G06F3/00 ,  G06N99/00

    CPC分类号： G06N99/005 ,  G06N7/005

    摘要： A method, information processing system, and computer readable article of manufacture model data. A first dataset is received that includes a first set of physical world data. At least one data model associated with the first dataset is generated based on the receiving. A second dataset is received that includes a second set of physical world data. The second dataset is compared to the at least one data model. A probability that the second dataset is modeled by the at least one data model is determined. A determination is made that the probability is above a given threshold. A decision associated with the second dataset based on the at least one data model is generated in response to the probability being above the given threshold. The probability and the decision are stored in memory. The probability and the decision are provided to user via a user interface.

    摘要翻译： 一种方法，信息处理系统和计算机可读物品的制造模型数据。 接收包括第一组物理世界数据的第一数据集。 基于接收生成与第一数据集相关联的至少一个数据模型。 接收包括第二组物理世界数据的第二数据集。 将第二数据集与至少一个数据模型进行比较。 确定第二数据集由至少一个数据模型建模的概率。 确定概率高于给定阈值。 响应于高于给定阈值的概率，生成基于至少一个数据模型与第二数据集相关联的决定。 概率和决定存储在内存中。 通过用户界面向用户提供概率和决定。

    SYSTEMS AND METHODS FOR MODELING AND PROCESSING FUNCTIONAL MAGNETIC RESONANCE IMAGE DATA USING FULL-BRAIN VECTOR AUTO-REGRESSIVE MODEL
    5.
    发明申请
    SYSTEMS AND METHODS FOR MODELING AND PROCESSING FUNCTIONAL MAGNETIC RESONANCE IMAGE DATA USING FULL-BRAIN VECTOR AUTO-REGRESSIVE MODEL 有权
    使用全脑向量自动调节模型建模和处理功能磁共振图像数据的系统和方法

    公开(公告)号：US20130034277A1

    公开(公告)日：2013-02-07

    申请号：US13197011

    申请日：2011-08-03

    申请人： Guillermo A. Cecchi ,  Rahul Garg ,  Ravishankar Rao

    发明人： Guillermo A. Cecchi ,  Rahul Garg ,  Ravishankar Rao

    IPC分类号： G06K9/00 ,  G06G7/60

    CPC分类号： G06F19/3443 ,  G01R33/4806 ,  G06F19/00 ,  G06F19/321 ,  G06T7/0012 ,  G06T11/003 ,  G06T2207/10016 ,  G06T2207/10076 ,  G06T2207/10088 ,  G06T2207/30016 ,  G16H50/20 ,  G16H50/70

    摘要： Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification.

    摘要翻译： 使用多变量自回归模型来建模功能性磁共振图像数据集的系统和方法，其捕获数据中的时间动力学，并且创建代表体素相对于大脑活动的功能连通性的数据集的减少的表示。 使用多变量自回归模型处理原始时空数据，其中具有高权重的模型中的系数被保留为最好地描述全部时空数据的指标。 当数据的时间样本数量相对较少时，使用稀疏回归技术构建模型。 模型系数用于执行索引，预测和分类等数据处理功能。