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61. 发明授权

US06988107B2 Reducing and controlling sizes of model-based recognizers 失效
标题翻译：减少和控制基于模型的识别器的尺寸
公开(公告)号：US06988107B2
公开(公告)日：2006-01-17
申请号：US10185172
申请日：2002-06-28
申请人： Christopher Meek , Bo Thiesson , John R. Bennett
发明人： Christopher Meek , Bo Thiesson , John R. Bennett
IPC分类号： G06F17/30
CPC分类号： G06K9/00422 , Y10S707/99931 , Y10S707/99942
摘要： A technique for reducing a model database for use with handwriting recognizers. The model database is processed with a tuning set to identify a set of models that would result in the greatest character recognition accuracy. If further model database reduction is desired, the technique iteratively identifies smaller models that have the least adverse effect on the error rate. The technique continues identifying smaller models until a desired model database size has been achieved.
摘要翻译：一种用于减少用于手写识别器的模型数据库的技术。模型数据库使用调整集来处理，以识别将导致最大字符识别精度的一组模型。如果需要进一步减少模型数据库，则该技术迭代地识别对错误率具有最小不利影响的较小模型。该技术继续识别较小的模型，直到达到所需的模型数据库大小。

62. 发明申请

US20050234960A1 Automatic data perspective generation for a target variable 有权
标题翻译：为目标变量生成自动数据透视图
公开(公告)号：US20050234960A1
公开(公告)日：2005-10-20
申请号：US10824108
申请日：2004-04-14
申请人： David Chickering , Bo Thiesson , Carl Kadie , David Heckerman , Christopher Meek , Allan Folting , Eric Vigesaa
发明人： David Chickering , Bo Thiesson , Carl Kadie , David Heckerman , Christopher Meek , Allan Folting , Eric Vigesaa
IPC分类号： G06F19/00 , G06F17/00 , G06F17/30 , G06N5/04
CPC分类号： G06F17/30592 , G06F2216/03 , Y10S707/99936 , Y10S707/99943
摘要： The present invention leverages machine learning techniques to provide automatic generation of conditioning variables for constructing a data perspective for a given target variable. The present invention determines and analyzes the best target variable predictors for a given target variable, employing them to facilitate the conveying of information about the target variable to a user. It automatically discretizes continuous and discrete variables utilized as target variable predictors to establish their granularity. In other instances of the present invention, a complexity and/or utility parameter can be specified to facilitate generation of the data perspective via analyzing a best target variable predictor versus the complexity of the conditioning variable(s) and/or utility. The present invention can also adjust the conditioning variables (i.e., target variable predictors) of the data perspective to provide an optimum view and/or accept control inputs from a user to guide/control the generation of the data perspective.
摘要翻译：本发明利用机器学习技术来提供用于为给定目标变量构建数据透视图的自动生成调节变量。本发明确定和分析给定目标变量的最佳目标变量预测变量，使用它们来促进向用户传达关于目标变量的信息。它自动离散化用作目标变量预测变量的连续和离散变量以确定其粒度。在本发明的其他实例中，可以规定复杂度和/或效用参数，以通过分析最佳目标变量预测器与调节变量和/或效用的复杂性来促进数据透视图的产生。本发明还可以调整数据透视图的调节变量（即，目标变量预测器），以提供最佳视图和/或接受来自用户的控制输入以指导/控制数据视角的产生。

63. 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。

64. 发明申请

US20050027665A1 Dynamic standardization for scoring linear regressions in decision trees 有权
标题翻译：在决策树中评分线性回归的动态标准化
公开(公告)号：US20050027665A1
公开(公告)日：2005-02-03
申请号：US10628546
申请日：2003-07-28
申请人： Bo Thiesson , David Chickering
发明人： Bo Thiesson , David Chickering
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00 , G06N3/08
CPC分类号： G06N99/005
摘要： The present invention relates to a system and method to facilitate data mining applications and automated evaluation of models for continuous variable data. In one aspect, a system is provided that facilitates decision tree learning. The system includes a learning component that generates non-standardized data that relates to a split in a decision tree and a scoring component that scores the split as if the non-standardized data at a subset of leaves of the decision tree had been shifted and/or scaled. A modification component can also be provided for a respective candidate split score on the decision tree, wherein the above data or data subset can be modified by shifting and/or scaling the data and a new score is computed on the modified data. Furthermore, an optimization component can be provided that analyzes the data and determines whether to treat the data as if it was: (1) shifted, (2) scaled, or (3) shifted and scaled.
摘要翻译：本发明涉及一种便于数据挖掘应用的系统和方法以及用于连续可变数据的模型的自动评估。在一个方面，提供了一种便于决策树学习的系统。该系统包括学习组件，该学习组件生成与决策树中的分割有关的非标准数据，以及评分组件，其如果在决策树的叶子的子集上的非标准化数据已被移位和/ 或缩放。还可以在决策树上为相应的候选分割分数提供修改组件，其中可以通过移动和/或缩放数据来修改上述数据或数据子集，并且对修改的数据计算新分数。此外，可以提供分析数据并确定是否对待数据的优化组件，如同是：（1）移位，（2）缩放，或（3）移位和缩放。

65. 发明授权

US06345265B1 Clustering with mixtures of bayesian networks 有权
标题翻译：聚类与贝叶斯网络的混合
公开(公告)号：US06345265B1
公开(公告)日：2002-02-05
申请号：US09220192
申请日：1998-12-23
申请人： Bo Thiesson , Christopher A. Meek , David Maxwell Chickering , David Earl Heckerman
发明人： Bo Thiesson , Christopher A. Meek , David Maxwell Chickering , David Earl Heckerman
IPC分类号： G06N302
CPC分类号： G06K9/6296 , G06N5/025 , Y10S707/99945 , Y10S707/99948
摘要： The invention employs mixtures of Bayesian networks to perform clustering. 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. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing. The invention determines membership of an individual case in a cluster based upon a set of data of plural individual cases by first learning the structure and parameters of an MBN given that data and then using the MBN to compute the probability of each HSBN generating the data of the individual case.
摘要翻译：本发明采用贝叶斯网络的混合来执行聚类。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。 MBN中的HSBN的数量对应于公共外部隐藏变量的状态数，并且每个HSBN基于公共外部隐藏变量在这些状态中的相应一个状态中的假设。在本发明的一种模式中，选择具有最高MBN分数的MBN用于执行推定。本发明通过首先学习给定该数据的MBN的结构和参数，然后使用MBN来计算生成数据的每个HSBN的概率，从而基于多个单独情况的一组数据来确定集群中的个别情况的成员资格个别情况。

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