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1. 发明授权

US06216134B1 Method and system for visualization of clusters and classifications 失效
标题翻译：集群和分类可视化的方法和系统
公开(公告)号：US06216134B1
公开(公告)日：2001-04-10
申请号：US09104751
申请日：1998-06-25
申请人： David E. Heckerman , David Maxwell Chickering , Usama M. Fayyad , Christopher A. Meek
发明人： David E. Heckerman , David Maxwell Chickering , Usama M. Fayyad , Christopher A. Meek
IPC分类号： G06F1730
CPC分类号： G06F17/30713 , G06Q30/0643 , Y10S707/956 , Y10S707/99934 , Y10S707/99935 , Y10S707/99944 , Y10S707/99945
摘要： A system that provides for the graphic visualization of the categories of a collection of records. The graphic visualization is referred to as “category graph.” The system optionally displays the category graph as a “similarity graph” or a “hierarchical map.” When displaying a category graph, the system displays a graphic representation of each category. The system displays the category graph as a similarity graph or a hierarchical map in a way that visually illustrates the similarity between categories. The display of a category graph allows a data analyst to better understand the similarity and dissimilarity between categories. A similarity graph includes a node for each category and an arc connecting nodes representing categories whose similarity is above a threshold. A hierarchical map is a tree structure that includes a node for each base category along with nodes representing combinations of similar categories.
摘要翻译：提供记录集合的类别的图形可视化的系统。图形可视化被称为“类别图”。系统可选地将类别图显示为“相似图”或“分层图”。当显示类别图时，系统显示每个类别的图形表示。系统以类似的方式显示类别图作为相似图或分层图，以视觉方式显示类别之间的相似性。类别图的显示允许数据分析人员更好地了解类别之间的相似性和不相似性。相似图包括用于每个类别的节点和连接表示相似度高于阈值的类别的节点的弧。层次映射是树结构，其包括每个基本类别的节点以及表示类似类别的组合的节点。

2. 发明授权

US07660705B1 Bayesian approach for learning regression decision graph models and regression models for time series analysis 有权
标题翻译：用于学习回归决策图模型的贝叶斯方法和时间序列分析的回归模型
公开(公告)号：US07660705B1
公开(公告)日：2010-02-09
申请号：US10102116
申请日：2002-03-19
申请人： Christopher A. Meek , David E. Heckerman , Robert L. Rounthwaite , David Maxwell Chickering , Bo Thiesson
发明人： Christopher A. Meek , David E. Heckerman , Robert L. Rounthwaite , David Maxwell Chickering , Bo Thiesson
IPC分类号： G06F17/10
CPC分类号： G06K9/6297
摘要： Methods and systems are disclosed for learning a regression decision graph model using a Bayesian model selection approach. In a disclosed aspect, the model structure and/or model parameters can be learned using a greedy search algorithm applied to grow the model so long as the model improves. This approach enables construction of a decision graph having a model structure that includes a plurality of leaves, at least one of which includes a non-trivial linear regression. The resulting model thus can be employed for forecasting, such as for time series data, which can include single or multi-step forecasting.
摘要翻译：公开了使用贝叶斯模型选择方法学习回归决策图模型的方法和系统。在公开的方面，只要模型改进，可以使用应用于增长模型的贪心搜索算法来学习模型结构和/或模型参数。该方法能够构建具有包括多个叶子的模型结构的决策图，其中至少一个包括非平凡的线性回归。因此，所得到的模型可以用于预测，例如用于时间序列数据，其可以包括单步或多步预测。

3. 发明授权

US06807537B1 Mixtures of Bayesian networks 失效
标题翻译：贝叶斯网络的混合
公开(公告)号：US06807537B1
公开(公告)日：2004-10-19
申请号：US08985114
申请日：1997-12-04
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： One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. 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. In another mode of the invention, some or all of the MBNs are retained as a collection of MBNs which perform inferencing in parallel, their outputs being weighted in accordance with the corresponding MBN scores and the MBN collection output being the weighted sum of all the MBN outputs. In one application of the invention, collaborative filtering may be performed by defining the observed variables to be choices made among a sample of users and the hidden variables to be the preferences of those users.
摘要翻译：本发明的一个方面是构建贝叶斯网络的混合物。本发明的另一方面是使用贝叶斯网络的这种混合来执行推理。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。在本发明的另一模式中，一些或所有MBN被保留为并行执行推论的MBN的集合，其输出根据相应的MBN分数加权，并且MBN收集输出是所有MBN的加权和输出。在本发明的一个应用中，可以通过将观察到的变量定义为在用户样本中作出的选择和作为这些用户的偏好的隐藏变量来执行协同过滤。

4. 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。

5. 发明授权

US06496816B1 Collaborative filtering with mixtures of bayesian networks 有权
标题翻译：使用贝叶斯网络混合进行协同过滤
公开(公告)号：US06496816B1
公开(公告)日：2002-12-17
申请号：US09220199
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： One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. 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. In another mode of the invention, some or all of the MBNs are retained as a collection of MBNs which perform inferencing in parallel, their outputs being weighted in accordance with the corresponding MBN scores and the MBN collection output being the weighted sum of all the MBN outputs. In one application of the invention, collaborative filtering may be performed by defining the observed variables to be choices made among a sample of users and the hidden variables to be the preferences of those users.
摘要翻译：本发明的一个方面是构建贝叶斯网络的混合物。本发明的另一方面是使用贝叶斯网络的这种混合来执行推理。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。在本发明的另一模式中，一些或所有MBN被保留为并行执行推论的MBN的集合，其输出根据相应的MBN分数加权，并且MBN收集输出是所有MBN的加权和输出。在本发明的一个应用中，可以通过将观察到的变量定义为在用户样本中作出的选择和作为这些用户的偏好的隐藏变量来执行协同过滤。

6. 发明授权

US6154736A Belief networks with decision graphs 失效
标题翻译：信念网络与决策图
公开(公告)号：US6154736A
公开(公告)日：2000-11-28
申请号：US902759
申请日：1997-07-30
申请人： David Maxwell Chickering , David Heckerman , Christopher A. Meek
发明人： David Maxwell Chickering , David Heckerman , Christopher A. Meek
IPC分类号： G06N5/04 , G06N7/00 , G06N5/00 , G06N5/02
CPC分类号： G06N7/005 , G06N5/04
摘要： An improved belief network is provided for assisting users in making decisions. The improved belief network utilizes a decision graph in each of its nodes to store the probabilities for that node. A decision graph is a much more flexible and efficient data structure for storing probabilities than either a tree or a table, because a decision graph can reflect any equivalence relationships between the probabilities and because leaf nodes having equivalent probabilities need not be duplicated. Additionally, by being able to reflect an equivalency relationship, multiple paths (or combinations of the parent values) refer to the same probability, which yields a more accurate probability.
摘要翻译：提供了一个改进的信念网络，用于帮助用户作出决策。改进的信念网络利用其每个节点中的决策图来存储该节点的概率。因为决策图可以反映概率之间的任何等价关系，并且因为具有等效概率的叶节点不需要重复，所以决策图是一种比树或表存储概率更灵活和高效的数据结构。另外，通过能够反映等价关系，多个路径（或父值的组合）指的是相同的概率，这产生更准确的概率。

7. 发明授权

US06336108B1 Speech recognition with mixtures of bayesian networks 有权
标题翻译：语音识别与贝叶斯网络的混合
公开(公告)号：US06336108B1
公开(公告)日：2002-01-01
申请号：US09220197
申请日：1998-12-23
申请人： Bo Thiesson , Christopher A. Meek , David Maxwell Chickering , David Earl Heckerman , Fileno A. Alleva , Mei-Yuh Hwang
发明人： Bo Thiesson , Christopher A. Meek , David Maxwell Chickering , David Earl Heckerman , Fileno A. Alleva , Mei-Yuh Hwang
IPC分类号： G06F1518
CPC分类号： G06K9/6296 , G06N5/025 , Y10S707/99945 , Y10S707/99948
摘要： The invention performs speech recognition using an array of 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. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN models the world under the hypothesis that the common external hidden variable is in a corresponding one of those states. In accordance with the invention, the MBNs encode the probabilities of observing the sets of acoustic observations given the utterance of a respective one of said parts of speech. Each of the HSBNs encodes the probabilities of observing the sets of acoustic observations given the utterance of a respective one of the parts of speech and given a hidden common variable being in a particular state. Each HSBN has nodes corresponding to the elements of the acoustic observations. These nodes store probability parameters corresponding to the probabilities with causal links representing dependencies between ones of said nodes.
摘要翻译：本发明使用贝叶斯网络混合的阵列来执行语音识别。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。 MBN中的HSBN的数量对应于共同外部隐藏变量的状态数，并且每个HSBN在假设下共同的外部隐藏变量处于相应的一个状态的假设下对世界进行建模。根据本发明，MBN编码了考虑到所述话音部分中的相应一个的话语来观察声学观测组的概率。每个HSBN编码观察给定语音相应的一个语音的发音并给出隐藏的公共变量处于特定状态的声学观察组的概率。每个HSBN具有对应于声学观测元素的节点。这些节点存储对应于概率的概率参数，其中因果链接表示所述节点之间的依赖关系。

8. 发明授权

US06408290B1 Mixtures of bayesian networks with decision graphs 失效
标题翻译：贝叶斯网络与决策图的混合
公开(公告)号：US06408290B1
公开(公告)日：2002-06-18
申请号：US09220200
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： One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. 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. In another mode of the invention, some or all of the MBNs are retained as a collection of MBNs which perform inferencing in parallel, their outputs being weighted in accordance with the corresponding MBN scores and the MBN collection output being the weighted sum of all the MBN outputs. In one application of the invention, collaborative filtering may be performed by defining the observed variables to be choices made among a sample of users and the hidden variables to be the preferences of those users.
摘要翻译：本发明的一个方面是构建贝叶斯网络的混合物。本发明的另一方面是使用贝叶斯网络的这种混合来执行推理。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。在本发明的另一模式中，一些或所有MBN被保留为并行执行推论的MBN的集合，其输出根据相应的MBN分数加权，并且MBN收集输出是所有MBN的加权和输出。在本发明的一个应用中，可以通过将观察到的变量定义为在用户样本中作出的选择和作为这些用户的偏好的隐藏变量来执行协同过滤。

9. 发明授权

US07983959B2 Systems and methods for estimating placement positions of content items on a rendered page 有权
标题翻译：用于估计呈现页面上的内容项目的展示位置的系统和方法
公开(公告)号：US07983959B2
公开(公告)日：2011-07-19
申请号：US11000243
申请日：2004-11-30
申请人： David M. Chickering , Christopher A. Meek , David E. Heckerman , Brian Burdick , Li Li , Murali Vajjiravel , Ying Li , Rajeev Prasad , Raxit A. Kagalwala , Tarek Najm , Sachin Dhawan
发明人： David M. Chickering , Christopher A. Meek , David E. Heckerman , Brian Burdick , Li Li , Murali Vajjiravel , Ying Li , Rajeev Prasad , Raxit A. Kagalwala , Tarek Najm , Sachin Dhawan
IPC分类号： G06Q30/00
CPC分类号： G06Q30/02 , G06Q30/0241 , G06Q30/0247 , G06Q30/0256 , G06Q30/0601 , G06Q30/08 , G06Q40/04
摘要： Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and/or images, on a rendered page relative to other content items on the page based upon bid value.
摘要翻译：用于确定由内容提供商针对在给定上下文呈现的页面（例如，网页）上的放置位置的出价的价值的系统和方法，例如在搜索引擎网络上发起的特定查询的搜索结果列表现场提供。此外，提供了系统和方法，用于基于出价值，在相对于页面上的其他内容项目的呈现页面上确定内容项目（例如广告和/或图像）的放置。

10. 发明授权

US07930353B2 Trees of classifiers for detecting email spam 有权
标题翻译：用于检测电子邮件垃圾邮件的分类树
公开(公告)号：US07930353B2
公开(公告)日：2011-04-19
申请号：US11193691
申请日：2005-07-29
申请人： David M. Chickering , Geoffrey J. Hulten , Robert L. Rounthwaite , Christopher A. Meek , David E. Heckerman , Joshua T. Goodman
发明人： David M. Chickering , Geoffrey J. Hulten , Robert L. Rounthwaite , Christopher A. Meek , David E. Heckerman , Joshua T. Goodman
IPC分类号： G06F15/16
CPC分类号： H04L51/12
摘要： Decision trees populated with classifier models are leveraged to provide enhanced spam detection utilizing separate email classifiers for each feature of an email. This provides a higher probability of spam detection through tailoring of each classifier model to facilitate in more accurately determining spam on a feature-by-feature basis. Classifiers can be constructed based on linear models such as, for example, logistic-regression models and/or support vector machines (SVM) and the like. The classifiers can also be constructed based on decision trees. “Compound features” based on internal and/or external nodes of a decision tree can be utilized to provide linear classifier models as well. Smoothing of the spam detection results can be achieved by utilizing classifier models from other nodes within the decision tree if training data is sparse. This forms a base model for branches of a decision tree that may not have received substantial training data.
摘要翻译：利用分类器模型填充的决策树利用电子邮件的每个功能使用单独的电子邮件分类器来提供增强的垃圾邮件检测。这通过定制每个分类器模型提供了更高的垃圾邮件检测的概率，以便于在逐个特征的基础上更准确地确定垃圾邮件。分类器可以基于诸如逻辑回归模型和/或支持向量机（SVM）等线性模型来构建。分类器也可以基于决策树构建。基于决策树的内部和/或外部节点的“复合特征”也可以用于提供线性分类器模型。垃圾邮件检测结果的平滑可以通过使用来自决策树内的其他节点的分类器模型来实现，如果训练数据是稀疏的。这形成了可能没有接收到大量训练数据的决策树的分支的基本模型。

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