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1. 发明申请

US20070189277A1 Virtual audio drivers and a virtual audio telephone interface 审中-公开
标题翻译：虚拟音频驱动程序和虚拟音频电话接口
公开(公告)号：US20070189277A1
公开(公告)日：2007-08-16
申请号：US11342586
申请日：2006-01-31
申请人： Cheng-Jen Yang , Mark Zhang
发明人： Cheng-Jen Yang , Mark Zhang
IPC分类号： H04L12/66
CPC分类号： H04L12/66
摘要： The present invention provides a system with virtual audio driver for the VoIP (voice over internet protocol) environment. The virtual audio driver is computer software, and it can be implemented by diverse possibilities. Including being implemented by different programming languages; supporting various operating systems (OS) and so on. It should be appreciated that the virtual audio driver is used for transferring the voice data, instead of producing the sounds. Besides, the present invention also provides a virtual telephone interface. After combining the virtual audio drivers and the virtual telephone interface with the VoIP client applications, users can simply answer VoIP calls from several VoIP client applications by single software.
摘要翻译：本发明提供一种具有用于VoIP（因特网语音协议）环境的虚拟音频驱动器的系统。虚拟音频驱动程序是计算机软件，可以通过多种可能性实现。包括由不同的编程语言实现; 支持各种操作系统（OS）等。应当理解，虚拟音频驱动器用于传送语音数据，而不是产生声音。此外，本发明还提供一种虚拟电话接口。将虚拟音频驱动程序和虚拟电话接口与VoIP客户端应用程序相结合后，用户可以通过单一软件简单地应答来自多个VoIP客户端应用程序的VoIP呼叫。

2. 发明申请

US20140122039A1 PATTERN CHANGE DISCOVERY BETWEEN HIGH DIMENSIONAL DATA SETS 审中-公开
标题翻译：高维数据集之间的模式变化发现
公开(公告)号：US20140122039A1
公开(公告)日：2014-05-01
申请号：US14060743
申请日：2013-10-23
申请人： Yi Xu , Zhongfei Mark Zhang
发明人： Yi Xu , Zhongfei Mark Zhang
IPC分类号： G06F17/50
CPC分类号： G06F17/18 , G06K9/6232
摘要： The general problem of pattern change discovery between high-dimensional data sets is addressed by considering the notion of the principal angles between the subspaces is introduced to measure the subspace difference between two high-dimensional data sets. Current methods either mainly focus on magnitude change detection of low-dimensional data sets or are under supervised frameworks. Principal angles bear a property to isolate subspace change from the magnitude change. To address the challenge of directly computing the principal angles, matrix factorization is used to serve as a statistical framework and develop the principle of the dominant subspace mapping to transfer the principal angle based detection to a matrix factorization problem. Matrix factorization can be naturally embedded into the likelihood ratio test based on the linear models. The method may be unsupervised and addresses the statistical significance of the pattern changes between high-dimensional data sets.
摘要翻译：高维数据集之间的模式变化发现的一般问题是通过考虑子空间之间的主角的概念来解决，以测量两个高维数据集之间的子空间差异。当前的方法主要集中在低维数据集的幅度变化检测或处于监督框架下。主角具有将子空间变化与幅度变化隔离的特性。为了解决直接计算主角的挑战，矩阵分解被用作统计框架，并开发了主要子空间映射的原理，将基于主角的检测转移到矩阵分解问题。矩阵分解可以自然地嵌入到基于线性模型的似然比检验中。该方法可以是无监督的，并且解决了高维数据集之间的模式变化的统计意义。

3. 发明授权

US08527432B1 Semi-supervised learning based on semiparametric regularization 有权
标题翻译：基于半参数正则化的半监督学习
公开(公告)号：US08527432B1
公开(公告)日：2013-09-03
申请号：US12538849
申请日：2009-08-10
申请人： Zhen Guo , Zhongfei (Mark) Zhang
发明人： Zhen Guo , Zhongfei (Mark) Zhang
IPC分类号： G06F15/18 , G06E1/00 , G06E3/00 , G06G7/00
CPC分类号： G06N99/005
摘要： Semi-supervised learning plays an important role in machine learning and data mining. The semi-supervised learning problem is approached by developing semiparametric regularization, which attempts to discover the marginal distribution of the data to learn the parametric function through exploiting the geometric distribution of the data. This learned parametric function can then be incorporated into the supervised learning on the available labeled data as the prior knowledge. A semi-supervised learning approach is provided which incorporates the unlabeled data into the supervised learning by a parametric function learned from the whole data including the labeled and unlabeled data. The parametric function reflects the geometric structure of the marginal distribution of the data. Furthermore, the proposed approach which naturally extends to the out-of-sample data is an inductive learning method in nature.
摘要翻译：半监督学习在机器学习和数据挖掘中起着重要的作用。半监督学习问题是通过开发半参数正则化来实现的，它试图通过利用数据的几何分布来发现数据的边缘分布来学习参数函数。然后将该学习的参数函数作为现有知识并入到可用标记数据的监督学习中。提供了一种半监督学习方法，其通过从包括标记和未标记数据的整个数据中学习的参数函数将未标记的数据合并到监督学习中。参数函数反映数据边际分布的几何结构。此外，自然地扩展到样本外数据的提出的方法本质上是归纳学习方法。

4. 发明授权

US08463053B1 Enhanced max margin learning on multimodal data mining in a multimedia database 有权
标题翻译：增强多媒体数据库中多模态数据挖掘的最大利润率学习
公开(公告)号：US08463053B1
公开(公告)日：2013-06-11
申请号：US12538845
申请日：2009-08-10
申请人： Zhen Guo , Zhongfei (Mark) Zhang
发明人： Zhen Guo , Zhongfei (Mark) Zhang
IPC分类号： G06K9/62
CPC分类号： G06F17/30256 , G06F17/10 , G06F17/30 , G06F17/30017 , G06K9/62 , G06K9/6218 , G06K9/6269 , G06K9/629
摘要： Multimodal data mining in a multimedia database is addressed as a structured prediction problem, wherein mapping from input to the structured and interdependent output variables is learned. A system and method for multimodal data mining is provided, comprising defining a multimodal data set comprising image information; representing image information of a data object as a set of feature vectors in a feature space; clustering in the feature space to group similar features; associating a non-image representation with a respective image data object based on the clustering; determining a joint feature representation of a respective data object as a mathematical weighted combination of a set of components of the joint feature representation; optimizing a weighting for a plurality of components of the mathematical weighted combination with respect to a prediction error between a predicted classification and a training classification; and employing the mathematical weighted combination for automatically classifying a new data object.
摘要翻译：在多媒体数据库中的多模式数据挖掘被解决为结构化预测问题，其中从输入到结构化和相互依赖的输出变量的映射被学习。提供了一种用于多模式数据挖掘的系统和方法，包括定义包括图像信息的多模式数据集; 将数据对象的图像信息表示为特征空间中的一组特征向量; 聚类在特征空间中组合相似特征; 基于聚类将非图像表示与相应的图像数据对象相关联; 确定相应数据对象的联合特征表示作为所述联合特征表示的一组分量的数学加权组合; 针对预测分类和训练分类之间的预测误差优化数学加权组合的多个分量的权重; 并采用数学加权组合来自动分类新的数据对象。

5. 发明授权

US08285719B1 System and method for probabilistic relational clustering 有权
标题翻译：概率关系聚类的系统和方法
公开(公告)号：US08285719B1
公开(公告)日：2012-10-09
申请号：US12538835
申请日：2009-08-10
申请人： Bo Long , Zhongfei (Mark) Zhang
发明人： Bo Long , Zhongfei (Mark) Zhang
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30598 , G06N7/005 , G06N99/005
摘要： Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects, such as Web mining, search marketing, bioinformatics, citation analysis, and epidemiology. A probabilistic model is presented for relational clustering, which also provides a principal framework to unify various important clustering tasks including traditional attributes-based clustering, semi-supervised clustering, co-clustering and graph clustering. The model seeks to identify cluster structures for each type of data objects and interaction patterns between different types of objects. Under this model, parametric hard and soft relational clustering algorithms are provided under a large number of exponential family distributions. The algorithms are applicable to relational data of various structures and at the same time unify a number of state-of-the-art clustering algorithms: co-clustering algorithms, the k-partite graph clustering, and semi-supervised clustering based on hidden Markov random fields.
摘要翻译：关系聚类由于其在涉及多类型相关数据对象，如Web挖掘，搜索营销，生物信息学，引文分析和流行病学等各种重要应用中的显着影响而引起越来越多的关注。提出了关系聚类的概率模型，为统一各种重要的聚类任务提供了一个主要框架，包括传统的基于属性的聚类，半监督聚类，共聚类和图聚类。该模型旨在确定不同类型对象之间的每种类型的数据对象和交互模式的集群结构。在这个模型下，在大量的指数族分布下提供了参数化的硬和软关系聚类算法。该算法适用于各种结构的关系数据，同时统一了许多最先进的聚类算法：共聚类算法，k-分块图聚类和基于隐马尔可夫的半监督聚类随机字段

6. 发明授权

US08195734B1 Combining multiple clusterings by soft correspondence 有权
标题翻译：通过软对应组合多个集群
公开(公告)号：US08195734B1
公开(公告)日：2012-06-05
申请号：US11945956
申请日：2007-11-27
申请人： Bo Long , Zhongfei Mark Zhang
发明人： Bo Long , Zhongfei Mark Zhang
IPC分类号： G06F7/32
CPC分类号： G06F17/30598
摘要： Combining multiple clusterings arises in various important data mining scenarios. However, finding a consensus clustering from multiple clusterings is a challenging task because there is no explicit correspondence between the classes from different clusterings. Provided is a framework based on soft correspondence to directly address the correspondence problem in combining multiple clusterings. Under this framework, an algorithm iteratively computes the consensus clustering and correspondence matrices using multiplicative updating rules. This algorithm provides a final consensus clustering as well as correspondence matrices that gives intuitive interpretation of the relations between the consensus clustering and each clustering from clustering ensembles. Extensive experimental evaluations demonstrate the effectiveness and potential of this framework as well as the algorithm for discovering a consensus clustering from multiple clusterings.
摘要翻译：在各种重要的数据挖掘方案中，组合了多个集群。然而，从多个集群中找到共识聚类是一项具有挑战性的任务，因为不同集群的类之间没有明确的对应关系。提供了一种基于软对应的框架，直接解决组合多个集群的对应问题。在这个框架下，算法使用乘法更新规则迭代地计算共享聚类和对应矩阵。该算法提供最终的一致聚类以及对应矩阵，从而可以直观地解释聚类集合中的共聚集和聚类之间的关系。广泛的实验评估表明了该框架的有效性和潜力，以及从多个聚类中发现共聚集的算法。

7. 发明授权

US08499022B1 Combining multiple clusterings by soft correspondence 有权
标题翻译：通过软对应组合多个集群
公开(公告)号：US08499022B1
公开(公告)日：2013-07-30
申请号：US13476100
申请日：2012-05-21
申请人： Bo Long , Zhongfei Mark Zhang
发明人： Bo Long , Zhongfei Mark Zhang
IPC分类号： G06F7/32
CPC分类号： G06F17/30598
摘要： Combining multiple clusterings arises in various important data mining scenarios. However, finding a consensus clustering from multiple clusterings is a challenging task because there is no explicit correspondence between the classes from different clusterings. Provided is a framework based on soft correspondence to directly address the correspondence problem in combining multiple clusterings. Under this framework, an algorithm iteratively computes the consensus clustering and correspondence matrices using multiplicative updating rules. This algorithm provides a final consensus clustering as well as correspondence matrices that gives intuitive interpretation of the relations between the consensus clustering and each clustering from clustering ensembles. Extensive experimental evaluations demonstrate the effectiveness and potential of this framework as well as the algorithm for discovering a consensus clustering from multiple clusterings.
摘要翻译：在各种重要的数据挖掘方案中，组合了多个集群。然而，从多个集群中找到共识聚类是一项具有挑战性的任务，因为不同集群的类之间没有明确的对应关系。提供了一种基于软对应的框架，直接解决组合多个集群的对应问题。在这个框架下，算法使用乘法更新规则迭代地计算共享聚类和对应矩阵。该算法提供最终的一致聚类以及对应矩阵，从而可以直观地解释聚类集合中的共聚集和聚类之间的关系。广泛的实验评估表明了该框架的有效性和潜力，以及从多个聚类中发现共聚集的算法。

8. 发明授权

US07970168B1 Hierarchical static shadow detection method 有权
标题翻译：分层静态阴影检测方法
公开(公告)号：US07970168B1
公开(公告)日：2011-06-28
申请号：US12911485
申请日：2010-10-25
申请人： Jian Yao , Zhong Fei (Mark) Zhang
发明人： Jian Yao , Zhong Fei (Mark) Zhang
IPC分类号： G06K9/00 , H04N7/18
CPC分类号： G06K9/342 , G06K9/4652
摘要： There is provided a hierarchical shadow detection system for color aerial images. The system performs well with highly complex images as well as images having different brightness and illumination conditions. The system consists of two hierarchical levels of processing. The first level involves, pixel level classification, through modeling the image as a reliable lattice and then maximizing the lattice reliability using the EM algorithm. Next, region level verification, through further exploiting the domain knowledge is performed. Further analyses show that the MRF model based segmentation is a special case of the pixel level classification model. A quantitative comparison of the system and a state-of-the-art shadow detection algorithm clearly indicates that the new system is highly effective in detecting shadow regions in an image under different illumination and brightness conditions.
摘要翻译：提供了一种用于彩色航空图像的分层阴影检测系统。该系统对高度复杂的图像以及具有不同亮度和照明条件的图像表现良好。该系统由两个层次级别的处理组成。第一级涉及像素级别分类，通过将图像建模为可靠的格子，然后使用EM算法最大化格点可靠性。接下来，通过进一步利用域知识进行区域级验证。进一步的分析表明，基于MRF模型的分割是像素级分类模型的特例。系统和最先进的阴影检测算法的定量比较清楚地表明，新系统在不同照明和亮度条件下检测图像中的阴影区域是非常有效的。

9. 发明申请

US20080260132A1 Low bandwidth but high capacity telephone conference system 审中-公开
标题翻译：低带宽但高容量的电话会议系统
公开(公告)号：US20080260132A1
公开(公告)日：2008-10-23
申请号：US11785851
申请日：2007-04-20
申请人： Mark Zhang , Cheng-Jen Yang
发明人： Mark Zhang , Cheng-Jen Yang
IPC分类号： H04M3/42
CPC分类号： H04M3/56 , H04M7/006
摘要： A teleconference system comprises VoIP server, a rootnode presider terminal, a first level conference element and a second level conference element. The first level conference element includes the rootnode presider terminal and at least one first participant terminals as childnode of the rootnode presider terminal, wherein at least one of the first participant terminals is the candidate to be selected as second level presider terminal. The second level conference element includes the second level presider terminal and at least one second participant terminals as childnode of the second level presider terminal. The VoIP server is coupled to the rootnode presider terminal, first participant terminals and second participant terminals.
摘要翻译：电话会议系统包括VoIP服务器，根节点主持终端，第一级会议元素和第二级会议元素。第一级会议元件包括根节点主叫终端和至少一个第一参与者终端作为根节点主动终端的子节点，其中，第一参与者终端中的至少一个是要被选择为第二级主持终端的候选者。第二级会议元件包括第二级主持人终端和至少一个第二参与者终端作为第二级主持人终端的子节点。 VoIP服务器耦合到根节点主持终端，第一参与者终端和第二参与者终端。

10. 发明授权

US07366323B1 Hierarchical static shadow detection method 有权
标题翻译：分层静态阴影检测方法
公开(公告)号：US07366323B1
公开(公告)日：2008-04-29
申请号：US10782230
申请日：2004-02-19
申请人： Jian Yao , ZhongFei (Mark) Zhang
发明人： Jian Yao , ZhongFei (Mark) Zhang
IPC分类号： G06K9/00
CPC分类号： G06K9/342 , G06K9/4652
摘要： There is provided a hierarchical shadow detection system for color aerial images. The system performs well with highly complex images as well as images having different brightness and illumination conditions. The system consists of two hierarchical levels of processing. The first level involves, pixel level classification, through modeling the image as a reliable lattice and then maximizing the lattice reliability using the EM algorithm. Next, region level verification, through further exploiting the domain knowledge is performed. Further analysis show that the MRF model based segmentation is a special case of the pixel level classification model. A quantitative comparison of the system and a state-of-the-art shadow detection algorithm clearly indicates that the new system is highly effective in detecting shadow regions in an image under different illumination and brightness conditions.
摘要翻译：提供了一种用于彩色航空图像的分层阴影检测系统。该系统对高度复杂的图像以及具有不同亮度和照明条件的图像表现良好。该系统由两个层次级别的处理组成。第一级涉及像素级别分类，通过将图像建模为可靠的格子，然后使用EM算法最大化格点可靠性。接下来，通过进一步利用域知识进行区域级验证。进一步分析表明，基于MRF模型的分割是像素级分类模型的特例。系统和最先进的阴影检测算法的定量比较清楚地表明，新系统在不同照明和亮度条件下检测图像中的阴影区域是非常有效的。

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