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1. 发明授权

US06694301B1 Goal-oriented clustering 有权
标题翻译：面向目标的聚类
公开(公告)号：US06694301B1
公开(公告)日：2004-02-17
申请号：US09540255
申请日：2000-03-31
申请人： David E. Heckerman , D. Maxwell Chickering , John C. Platt , Christopher A. Meek , Bo Thiesson
发明人： David E. Heckerman , D. Maxwell Chickering , John C. Platt , Christopher A. Meek , Bo Thiesson
IPC分类号： G06N502
CPC分类号： G06K9/6254 , G06K9/6226 , G06K9/6296 , G06N7/005
摘要： Clustering for purposes of data visualization and making predictions is disclosed. Embodiments of the invention are operable on a number of variables that have a predetermined representation. The variables include input-only variables, output-only variables, and both input-and-output variables. Embodiments of the invention generate a model that has a bottleneck architecture. The model includes a top layer of nodes of at least the input-only variables, one or more middle layer of hidden nodes, and a bottom layer of nodes of the output-only and the input-and-output variables. At least one cluster is determined from this model. The model can be a probabilistic neural network and/or a Bayesian network.
摘要翻译：公开了用于数据可视化和进行预测的聚类。本发明的实施例可以对具有预定表示的多个变量进行操作。变量包括仅输入变量，仅输出变量，以及输入和输出变量。本发明的实施例生成具有瓶颈架构的模型。该模型包括至少仅输入变量，一个或多个中间层隐藏节点的顶层，以及仅输出和输入和输出变量的节点的底层。从该模型确定至少一个群集。该模型可以是概率神经网络和/或贝叶斯网络。

2. 发明授权

US06321225B1 Abstracting cooked variables from raw variables 有权
标题翻译：从原始变量抽取熟变量
公开(公告)号：US06321225B1
公开(公告)日：2001-11-20
申请号：US09298598
申请日：1999-04-23
申请人： David E. Heckerman , D. Maxwell Chickering , Christopher A. Meek , Robert L. Rounthwaite
发明人： David E. Heckerman , D. Maxwell Chickering , Christopher A. Meek , Robert L. Rounthwaite
IPC分类号： G06F1730
CPC分类号： G06F17/30539 , Y10S707/99934 , Y10S707/99936 , Y10S707/99942 , Y10S707/99945 , Y10S707/99952
摘要： A method and a system for abstracting cooked variables from raw variables. In one embodiment, a data set that has a plurality of records is input into a system, where each record has a value for each of a plurality of raw transactional variables. These variables are organized into a hierarchy of nodes. The raw transactional variables are abstracted into a lesser number of cooked transactional variables, and the cooked transactional variables are output.
摘要翻译：从原始变量抽取煮熟变量的方法和系统。在一个实施例中，具有多个记录的数据集被输入到系统中，其中每个记录具有多个原始事务变量中的每一个的值。这些变量被组织成节点的层次结构。原始事务变量被抽象为较少数量的熟化事务变量，并且输出熟化的事务变量。

3. 发明授权

US06330563B1 Architecture for automated data analysis 有权
标题翻译：自动数据分析架构
公开(公告)号：US06330563B1
公开(公告)日：2001-12-11
申请号：US09298717
申请日：1999-04-23
申请人： David E. Heckerman , D. Maxwell Chickering , Christopher A. Meek , Robert L. Rounthwaite
发明人： David E. Heckerman , D. Maxwell Chickering , Christopher A. Meek , Robert L. Rounthwaite
IPC分类号： G06F1730
CPC分类号： G06N5/025 , G06F17/10 , G06F2216/03 , Y10S707/99935
摘要： An architecture for automated data analysis. In one embodiment, a computerized system comprising an automated problem formulation layer, a first learning engine, and a second learning engine. The automated problem formulation layer receives a data set. The data set has a plurality of records, where each record has a value for each of a plurality of raw transactional variables. The layer abstracts the raw transactional variables into cooked transactional variables. The first learning engine generates a model for the cooked transactional variables, while the second learning engine generates a model for the raw transactional variables.
摘要翻译：用于自动数据分析的架构。在一个实施例中，包括自动化问题制定层，第一学习引擎和第二学习引擎的计算机化系统。自动化问题制定层接收数据集。数据集具有多个记录，其中每个记录具有多个原始事务变量中的每一个的值。该层将原始事务变量抽象为熟的事务变量。第一个学习引擎为煮熟的事务变量生成模型，而第二个学习引擎生成原始事务变量的模型。

4. 发明授权

US06519599B1 Visualization of high-dimensional data 有权
标题翻译：高维数据的可视化
公开(公告)号：US06519599B1
公开(公告)日：2003-02-11
申请号：US09517138
申请日：2000-03-02
申请人： D. Maxwell Chickering , David E. Heckerman , Christopher A. Meek , Robert L. Rounthwaite , Amir Netz , Thierry D'Hers
发明人： D. Maxwell Chickering , David E. Heckerman , Christopher A. Meek , Robert L. Rounthwaite , Amir Netz , Thierry D'Hers
IPC分类号： G06F1730
CPC分类号： G06F17/30994 , Y10S707/99945
摘要： Visualization of high-dimensional data sets is disclosed, particularly the display of a network model for a data set. The network, such as a dependency or a Bayesian network, has a number of nodes having dependencies thereamong. The network can be displayed items and connections, corresponding to nodes and dependencies, respectively. Selection of a particular item in one embodiment results in the display of the local distribution associated with the node for the item. In one embodiment, only a predetermined number of the items are shown, such as only the items representing the most popular nodes. Furthermore, in one embodiment, in response to receiving a user input, a sub-set of the connections is displayed, proportional to the user input. In another embodiment, a particular item is displayed in an emphasized manner, and the particular connections representing dependencies including the node represented by the particular item, as well as the items representing nodes also in these dependencies, are also displayed in the emphasized manner. Furthermore, in one embodiment, only an indicated sub-set of the items is displayed.
摘要翻译：公开了高维数据集的可视化，特别是显示数据集的网络模型。诸如依赖关系或贝叶斯网络的网络具有多个具有依赖关系的节点。网络可以分别显示对应于节点和依赖关系的项目和连接。在一个实施例中，特定项目的选择导致与项目的节点相关联的本地分布的显示。在一个实施例中，仅显示预定数量的项目，诸如仅表示最受欢迎节点的项目。此外，在一个实施例中，响应于接收到用户输入，显示与用户输入成比例的连接的子集。在另一个实施例中，以强调方式显示特定项目，并且还以强调的方式显示表示依赖性的特定连接，包括由特定项目表示的节点以及表示节点的项目也在这些依赖关系中。此外，在一个实施例中，仅显示所指示的项目子集。

5. 发明授权

US07580813B2 Systems and methods for new time series model probabilistic ARMA 有权
标题翻译：新时间序列模型概率ARMA的系统和方法
公开(公告)号：US07580813B2
公开(公告)日：2009-08-25
申请号：US10463145
申请日：2003-06-17
申请人： Bo Thiesson , Christopher A. Meek , David M. Chickering , David E. Heckerman
发明人： Bo Thiesson , Christopher A. Meek , David M. Chickering , David E. Heckerman
IPC分类号： G06F17/50 , G05B23/02
CPC分类号： G06F17/18
摘要： The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.
摘要翻译：本发明利用交叉预测方案来预测离散和连续时间观测数据的值，其中每个连续时间管变量的条件方差固定为小的正值。通过在基于ARMA的模型中允许交叉预测，可以准确预测时间序列中连续和离散观测值。本发明通过扩展ARMA模型来实现这一目的，使得第一时间序列“管”用于促进或“交叉预测”第二时间序列管中的值以形成“ARMAxp”模型。一般来说，在ARMAxp模型中，每个连续变量的分布是仅在离散变量上分裂并具有在所有叶上具有连续回归的线性回归的决策图，并且每个离散变量的分布是仅分解为离散变量，并在所有叶子上具有额外的分布。

6. 发明授权

US07246048B2 Determining near-optimal block size for incremental-type expectation maximization (EM) algorithms 有权
标题翻译：确定增量型期望最大化（EM）算法的近似最优块大小
公开(公告)号：US07246048B2
公开(公告)日：2007-07-17
申请号：US11177734
申请日：2005-07-08
申请人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
发明人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
IPC分类号： G06F7/60
CPC分类号： G06K9/6226 , G06F17/10 , G06F17/18 , Y10S707/99942
摘要： Determining the near-optimal block size for incremental-type expectation maximization (EM) algorithms is disclosed. Block size is determined based on the novel insight that the speed increase resulting from using an incremental-type EM algorithm as opposed to the standard EM algorithm is roughly the same for a given range of block sizes. Furthermore, this block size can be determined by an initial version of the EM algorithm that does not reach convergence. For a current block size, the speed increase is determined, and if the speed increase is the greatest determined so far, the current block size is set as the target block size. This process is repeated for new block sizes, until no new block sizes can be determined.
摘要翻译：公开了确定增量型期望最大化（EM）算法的近似最小块大小。基于新的认识来确定块大小，即对于给定的块大小范围，使用增量型EM算法而不是标准EM算法导致的速度增加大致相同。此外，该块大小可以由未达到收敛的EM算法的初始版本来确定。对于当前块大小，确定速度增加，并且如果到目前为止确定的速度增加最大，则将当前块大小设置为目标块大小。对于新的块大小重复此过程，直到不能确定新的块大小。

7. 发明授权

US07200267B1 Handwriting recognition with mixtures of bayesian networks 有权
标题翻译：手写识别与贝叶斯网络混合
公开(公告)号：US07200267B1
公开(公告)日：2007-04-03
申请号：US11324444
申请日：2005-12-30
申请人： John Bennett , David E. Heckerman , Christopher A. Meek , Bo Thiesson
发明人： John Bennett , David E. Heckerman , Christopher A. Meek , Bo Thiesson
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/00422 , G06K9/6296
摘要： The invention performs handwriting recognition using mixtures of Bayesian networks. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. Each HSBN models the world under the hypothesis that the common external hidden variable is in a corresponding one of its states. The MBNs encode the probabilities of observing the sets of visual observations corresponding to a handwritten character. Each of the HSBNs encodes the probabilities of observing the sets of visual observations corresponding to a handwritten character and given a hidden common variable being in a particular state.
摘要翻译：本发明使用贝叶斯网络的混合来执行手写识别。贝叶斯网络（MBN）的混合由多个具有隐藏和观察变量的假设特定贝叶斯网络（HSBN）组成。常见的外部隐藏变量与MBN相关联，但不包括在任何HSBN中。每个HSBN在假设下共同的外部隐藏变量处于相应的一个状态的模型中模拟世界。 MBN编码观察对应于手写字符的视觉观察组的概率。每个HSBN编码观察对应于手写字符的视觉观察组的概率，并给出处于特定状态的隐藏的公共变量。

8. 发明授权

US07409371B1 Efficient determination of sample size to facilitate building a statistical model 有权
标题翻译：有效确定样本量以便建立统计模型
公开(公告)号：US07409371B1
公开(公告)日：2008-08-05
申请号：US09873719
申请日：2001-06-04
申请人： David E. Heckerman , Christopher A. Meek , Bo Thiesson
发明人： David E. Heckerman , Christopher A. Meek , Bo Thiesson
IPC分类号： G06N5/00
CPC分类号： G06N99/005
摘要： A model is constructed for an initial subset of the data using a first parameter estimation algorithm. The model may be evaluated, for example, by applying the model to a holdout data set of the data. If the model is not acceptable, additional data is added to the data subset and the first parameter estimation algorithm is repeated for the aggregate data subset. An appropriate subset of the data exists when the first parameter estimation algorithm produces an acceptable model. The appropriate subset of the data may then be employed by a second parameter estimation algorithm, which may be a more accurate version of the first algorithm or a different algorithm altogether, to build a statistical model to characterize the data.
摘要翻译：使用第一参数估计算法为数据的初始子集构建模型。可以例如通过将模型应用于数据的保持数据集来评估该模型。如果模型不可接受，则向数据子集添加附加数据，并且针对聚合数据子集重复第一参数估计算法。当第一参数估计算法产生可接受的模型时，存在数据的适当子集。然后可以通过第二参数估计算法来采用数据的适当子集，第二参数估计算法可以是第一算法的更准确的版本或者完全不同的算法，以构建用于表征数据的统计模型。

9. 发明授权

US07003158B1 Handwriting recognition with mixtures of Bayesian networks 失效
标题翻译：具有贝叶斯网络混合的手写识别
公开(公告)号：US07003158B1
公开(公告)日：2006-02-21
申请号：US10075962
申请日：2002-02-14
申请人： John Bennett , David E. Heckerman , Christopher A. Meek , Bo Thiesson
发明人： John Bennett , David E. Heckerman , Christopher A. Meek , Bo Thiesson
IPC分类号： G06K9/00
CPC分类号： G06K9/00422 , G06K9/6296
摘要： The invention performs handwriting recognition using mixtures of Bayesian networks. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. Each HSBN models the world under the hypothesis that the common external hidden variable is in a corresponding one of its states. The MBNs encode the probabilities of observing the sets of visual observations corresponding to a handwritten character. Each of the HSBNs encodes the probabilities of observing the sets of visual observations corresponding to a handwritten character and given a hidden common variable being in a particular state.
摘要翻译：本发明使用贝叶斯网络的混合来执行手写识别。贝叶斯网络（MBN）的混合由多个具有隐藏和观察变量的假设特定贝叶斯网络（HSBN）组成。常见的外部隐藏变量与MBN相关联，但不包括在任何HSBN中。每个HSBN在假设下共同的外部隐藏变量处于相应的一个状态的模型中模拟世界。 MBN编码观察对应于手写字符的视觉观察组的概率。每个HSBN编码观察对应于手写字符的视觉观察组的概率，并给出处于特定状态的隐藏的公共变量。

10. 发明授权

US06922660B2 Determining near-optimal block size for incremental-type expectation maximization (EM) algorithms 失效
标题翻译：确定增量型期望最大化（EM）算法的近似最优块大小
公开(公告)号：US06922660B2
公开(公告)日：2005-07-26
申请号：US09728508
申请日：2000-12-01
申请人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
发明人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
IPC分类号： G06F17/10 , G06F17/18
CPC分类号： G06K9/6226 , G06F17/10 , G06F17/18 , Y10S707/99942
摘要： Determining the near-optimal block size for incremental-type expectation maximization (EM) algorithms is disclosed. Block size is determined based on the novel insight that the speed increase resulting from using an incremental-type EM algorithm as opposed to the standard EM algorithm is roughly the same for a given range of block sizes. Furthermore, this block size can be determined by an initial version of the EM algorithm that does not reach convergence. For a current block size, the speed increase is determined, and if the speed increase is the greatest determined so far, the current block size is set as the target block size. This process is repeated for new block sizes, until no new block sizes can be determined.
摘要翻译：公开了确定增量型期望最大化（EM）算法的近似最小块大小。基于新的认识来确定块大小，即对于给定的块大小范围，使用增量型EM算法而不是标准EM算法导致的速度增加大致相同。此外，该块大小可以由未达到收敛的EM算法的初始版本来确定。对于当前块大小，确定速度增加，并且如果到目前为止确定的速度增加最大，则将当前块大小设置为目标块大小。对于新的块大小重复此过程，直到不能确定新的块大小。

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