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公开(公告)号：US08688417B2
公开(公告)日：2014-04-01
申请号：US13162927
申请日：2011-06-17
申请人： Alex Bocharov , Christopher A. Meek , Bo Thiesson
发明人： Alex Bocharov , Christopher A. Meek , Bo Thiesson
IPC分类号： G06F17/50
CPC分类号： G06F17/18
摘要： In one embodiment, an event impact signature detector may analyze a time series with external events. A data interface 250 may receive a data set 310 representing the time series with external events. A processor 220 may fit the data set 310 into a baseline time series model 330. The processor 220 may iteratively determine each event location 352 for multiple external events 350 affecting the baseline time series model 330. The processor 220 may iteratively solve for each event impact 354 of the multiple external events 350 factoring in interactions between the multiple external events 350.
摘要翻译： 在一个实施例中，事件影响签名检测器可以分析具有外部事件的时间序列。 数据接口250可以接收表示具有外部事件的时间序列的数据集310。 处理器220可以将数据集310拟合到基线时间序列模型330中。处理器220可以迭代地确定影响基线时间序列模型330的多个外部事件350的每个事件位置352.处理器220可以迭代地解决每个事件的影响 多个外部事件350之间的354个因素导致多个外部事件之间的交互350。
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公开(公告)号：US20120323537A1
公开(公告)日：2012-12-20
申请号：US13162927
申请日：2011-06-17
申请人： Alex Bocharov , Christopher A. Meek , Bo Thiesson
发明人： Alex Bocharov , Christopher A. Meek , Bo Thiesson
IPC分类号： G06F17/10
CPC分类号： G06F17/18
摘要： In one embodiment, an event impact signature detector may analyze a time series with external events. A data interface 250 may receive a data set 310 representing the time series with external events. A processor 220 may fit the data set 310 into a baseline time series model 330. The processor 220 may iteratively determine each event location 352 for multiple external events 350 affecting the baseline time series model 330. The processor 220 may iteratively solve for each event impact 354 of the multiple external events 350 factoring in interactions between the multiple external events 350.
摘要翻译： 在一个实施例中，事件影响签名检测器可以分析具有外部事件的时间序列。 数据接口250可以接收表示具有外部事件的时间序列的数据集310。 处理器220可以将数据集310拟合到基线时间序列模型330中。处理器220可以迭代地确定影响基线时间序列模型330的多个外部事件350的每个事件位置352.处理器220可以迭代地解决每个事件的影响 多个外部事件350之间的354个因素导致多个外部事件之间的交互350。
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公开(公告)号：US20090287626A1
公开(公告)日：2009-11-19
申请号：US12200648
申请日：2008-08-28
CPC分类号： G06F16/3322 , G10L15/26
摘要： A multi-modal search system (and corresponding methodology) is provided. The system employs text, speech, touch and gesture input to establish a search query. Additionally, a subset of the modalities can be used to obtain search results based upon exact or approximate matches to a search result. For example, wildcards, which can either be triggered by the user or inferred by the system, can be employed in the search.
摘要翻译： 提供了一种多模式搜索系统（及相应的方法）。 系统采用文字，语音，触摸和手势输入建立搜索查询。 此外，模态的子集可以用于基于与搜索结果的精确或近似匹配来获得搜索结果。 例如，可以由用户触发或由系统推断的通配符可用于搜索。
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公开(公告)号：US07580813B2
公开(公告)日：2009-08-25
申请号：US10463145
申请日：2003-06-17
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。
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公开(公告)号：US07548856B2
公开(公告)日：2009-06-16
申请号：US10441470
申请日：2003-05-20
申请人： Bo Thiesson , Christopher A. Meek
发明人： Bo Thiesson , Christopher A. Meek
IPC分类号： G10L15/06
CPC分类号： G06K9/6226 , G06K9/6296 , G10L15/08
摘要： The present invention utilizes a discriminative density model selection method to provide an optimized density model subset employable in constructing a classifier. By allowing multiple alternative density models to be considered for each class in a multi-class classification system and then developing an optimal configuration comprised of a single density model for each class, the classifier can be tuned to exhibit a desired characteristic such as, for example, high classification accuracy, low cost, and/or a balance of both. In one instance of the present invention, error graph, junction tree, and min-sum propagation algorithms are utilized to obtain an optimization from discriminatively selected density models.
摘要翻译： 本发明利用鉴别密度模型选择方法来提供可用于构建分类器的优化密度模型子集。 通过允许在多类分类系统中为每个类别考虑多个替代密度模型，然后开发由每个类别的单个密度模型组成的最佳配置，分类器可以被调谐以呈现期望的特性，例如 ，分类精度高，成本低，和/或两者的平衡。 在本发明的一个实例中，使用误差图，结树和最小和传播算法来从区分选择的密度模型中获得优化。
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公开(公告)号：US07460712B2
公开(公告)日：2008-12-02
申请号：US11672458
申请日：2007-02-07
申请人： Bo Thiesson , Christopher A. Meek
发明人： Bo Thiesson , Christopher A. Meek
CPC分类号： G06K9/6292 , G06K9/222
摘要： The present invention utilizes generic and user-specific features of handwriting samples to provide adaptive handwriting recognition with a minimum level of user-specific enrollment data. By allowing generic and user-specific classifiers to facilitate in a recognition process, the features of a specific user's handwriting can be exploited to quickly ascertain characteristics of handwriting characters not yet entered by the user. Thus, new characters can be recognized without requiring a user to first enter that character as enrollment or “training” data. In one instance of the present invention, processing of generic features is accomplished by a generic classifier trained on multiple users. In another instance of the present invention, a user-specific classifier is employed to modify a generic classifier's classification as required to provide user-specific handwriting recognition.
摘要翻译： 本发明利用手写样本的通用和用户特定的特征来提供具有最低级别的用户特定注册数据的自适应手写识别。 通过允许通用和用户特定的分类器便于识别过程，可以利用特定用户手写的特征来快速确定用户尚未输入的手写字符的特征。 因此，可以识别新的字符，而不需要用户首先将该字符输入作为注册或“训练”数据。 在本发明的一个实例中，通用特征的处理由对多个用户进行训练的通用分类器来完成。 在本发明的另一个实例中，使用用户特定的分类器根据需要修改通用分类器的分类以提供用户特定的手写识别。
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公开(公告)号：US20080140519A1
公开(公告)日：2008-06-12
申请号：US11608631
申请日：2006-12-08
CPC分类号： G06Q30/0222 , G06F16/81 , G06Q30/02
摘要： Match criteria are provided to specify when advertisements will be shown, for instance in a search environment. Input such as search queries can be represented in a simplified form such as an implicit and/or explicit wildcard expression. Advertisers or other entities can bid on terms such that advertisements or similar content are presented when the terms match an expansion of a simplified input. Matching ads can subsequently be displayed alone or in combination with query expansion suggestions and/or query results.
摘要翻译： 提供匹配标准以指定何时显示广告，例如在搜索环境中。 诸如搜索查询之类的输入可以以简单的形式表示，例如隐式和/或显式通配符表达式。 广告商或其他实体可以按照条款进行出价，使得当术语与简化输入的扩展相匹配时，呈现广告或类似内容。 随后可以单独显示匹配的广告，或与查询扩展建议和/或查询结果相结合。
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8.发明授权Determining near-optimal block size for incremental-type expectation maximization (EM) algorithms 有权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
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。
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公开(公告)号：US06807537B1
公开(公告)日：2004-10-19
申请号：US08985114
申请日：1997-12-04
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。
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10.发明授权公开(公告)号：US07596475B2
公开(公告)日：2009-09-29
申请号：US11005148
申请日：2004-12-06
申请人： Bo Thiesson , Christopher A. Meek
发明人： Bo Thiesson , Christopher A. Meek
CPC分类号： G06K9/6296
摘要： The subject invention leverages standard probabilistic inference techniques to determine a log-likelihood for a conditional Gaussian graphical model of a data set with at least one continuous variable and with data not observed for at least one of the variables. This provides an efficient means to compute gradients for CG models with continuous variables and incomplete data observations. The subject invention allows gradient-based optimization processes to employ gradients to iteratively adapt parameters of models in order to improve incomplete data log-likelihoods and identify maximum likelihood estimates (MLE) and/or local maxima of the incomplete data log-likelihoods. Conditional Gaussian local gradients along with conditional multinomial local gradients determined by the subject invention can be utilized to facilitate in providing parameter gradients for full conditional Gaussian models.
摘要翻译： 本发明利用标准概率推理技术来确定具有至少一个连续变量的数据集的条件高斯图形模型的对数似然，并且对于至少一个变量未观察到数据。 这为用于连续变量和不完整数据观察的CG模型计算梯度提供了有效手段。 本发明允许基于梯度的优化过程使用梯度来迭代地适应模型的参数，以便改进不完整的数据对数似然性并且识别不完全数据对数似然性的最大似然估计（MLE）和/或局部最大值。 条件高斯局部梯度以及由本发明确定的条件多项式局部梯度可以用于促进为全条件高斯模型提供参数梯度。
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