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61. 发明申请

US20060293899A1 Compressing language models with Golomb coding 有权
标题翻译：用Golomb编码压缩语言模型
公开(公告)号：US20060293899A1
公开(公告)日：2006-12-28
申请号：US11159712
申请日：2005-06-23
申请人： Kenneth Church , Bo Thiesson , Edward Hart
发明人： Kenneth Church , Bo Thiesson , Edward Hart
IPC分类号： G10L11/00
CPC分类号： G10L15/197
摘要： A method of compressing a language model is provided. A list of numerical values is generated from elements of a user input. The list of values is sorted. Differences between adjacent integer values in the list are calculated. Each calculated difference is encoded using a Golomb code.
摘要翻译：提供了一种压缩语言模型的方法。数值列表是从用户输入的元素生成的。值列表被排序。计算列表中相邻整数值之间的差异。每个计算的差值使用Golomb码进行编码。

62. 发明申请

US20060290535A1 Using language models to expand wildcards 有权
标题翻译：使用语言模型来扩展通配符
公开(公告)号：US20060290535A1
公开(公告)日：2006-12-28
申请号：US11159711
申请日：2005-06-23
申请人： Bo Thiesson , Kenneth Church
发明人： Bo Thiesson , Kenneth Church
IPC分类号： H03M11/00 , H03K17/94
CPC分类号： G06F17/276
摘要： A method of inputting text is provided in which a first portion of an input string is received from a user, the first portion of the input string including at least one keystroke representing a wildcard character of the input string. A second portion of the input string is then received, with the second portion including one or more keystrokes all representing non-wildcard characters of the input string.
摘要翻译：提供一种输入文本的方法，其中从用户接收输入字符串的第一部分，输入字符串的第一部分包括表示输入字符串的通配符的至少一个按键。然后接收输入字符串的第二部分，其中第二部分包括一个或多个击键，表示输入字符串的非通配符。

63. 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。

64. 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。

65. 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。

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